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<!doctype html><html data-n-head-ssr lang="en" data-n-head="%7B%22lang%22:%7B%22ssr%22:%22en%22%7D%7D"><head ><meta data-n-head="ssr" charset="utf-8"><meta data-n-head="ssr" name="viewport" content="width=device-width, initial-scale=1"><meta data-n-head="ssr" name="msapplication-TileColor" content="#247CB3"><meta data-n-head="ssr" name="msapplication-TileImage" content="https://asset.jmir.pub/assets/static/images/mstile-144x144.png"><meta data-n-head="ssr" name="description" content="Background: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. Objective: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. Methods: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. Results: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. Conclusions: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine. "><meta data-n-head="ssr" name="keywords" content="ethics; artificial intelligence; medicine; machine learning; health; communication; mobile phone; diagnosis; chatbot; cancer therapy; medical biophysics"><meta data-n-head="ssr" name="DC.Title" content="Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review"><meta data-n-head="ssr" name="DC.Subject" content="ethics; artificial intelligence; medicine; machine learning; health; communication; mobile phone; diagnosis; chatbot; cancer therapy; medical biophysics"><meta data-n-head="ssr" name="DC.Description" content="Background: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. Objective: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. Methods: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. Results: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. Conclusions: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine. "><meta data-n-head="ssr" name="DC.Publisher" content="JMIR Cancer"><meta data-n-head="ssr" name="DC.Publisher.Address" content="JMIR Publications // 130 Queens Quay East, Unit 1100 // Toronto, ON, M5A 0P6"><meta data-n-head="ssr" name="DC.Date" scheme="ISO8601" content="2021-11-29"><meta data-n-head="ssr" name="DC.Type" content="Text.Serial.Journal"><meta data-n-head="ssr" name="DC.Format" scheme="IMT" content="text/xml"><meta data-n-head="ssr" name="DC.Identifier" content="doi:10.2196/27850"><meta data-n-head="ssr" name="DC.Language" scheme="ISO639-1" content="EN"><meta data-n-head="ssr" name="DC.Relation" content="World"><meta data-n-head="ssr" name="DC.Source" content="JMIR Cancer 2021;7(4):e27850 https://cancer.jmir.org/2021/4/e27850"><meta data-n-head="ssr" name="DC.Rights" content="Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work ("first published in the Journal of Medical Internet Research...") is properly cited with original URL and bibliographic citation information. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included."><meta data-n-head="ssr" property="og:title" content="Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review"><meta data-n-head="ssr" property="og:type" content="article"><meta data-n-head="ssr" property="og:url" content="https://cancer.jmir.org/2021/4/e27850"><meta data-n-head="ssr" property="og:image" content="https://asset.jmir.pub/assets/1f6273f749f7b0fa0b4f42b4feaed538.png"><meta data-n-head="ssr" property="og:site_name" content="JMIR Cancer"><meta data-n-head="ssr" name="twitter:card" content="summary_large_image"><meta data-n-head="ssr" name="twitter:site" content="@jmirpub"><meta data-n-head="ssr" name="twitter:title" content="Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review"><meta data-n-head="ssr" name="twitter:description" content="Background: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. Objective: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. Methods: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. Results: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. Conclusions: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine. "><meta data-n-head="ssr" name="twitter:image" content="https://asset.jmir.pub/assets/1f6273f749f7b0fa0b4f42b4feaed538.png"><meta data-n-head="ssr" name="citation_title" content="Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review"><meta data-n-head="ssr" name="citation_journal_title" content="JMIR Cancer"><meta data-n-head="ssr" name="citation_publisher" content="JMIR Publications Inc., Toronto, Canada"><meta data-n-head="ssr" name="citation_doi" content="10.2196/27850"><meta data-n-head="ssr" name="citation_issue" content="4"><meta data-n-head="ssr" name="citation_volume" content="7"><meta data-n-head="ssr" name="citation_firstpage" content="e27850"><meta data-n-head="ssr" name="citation_date" content="2021-11-29"><meta data-n-head="ssr" name="citation_abstract_html_url" content="https://cancer.jmir.org/2021/4/e27850"><meta data-n-head="ssr" name="citation_abstract_pdf_url" content="https://cancer.jmir.org/2021/4/e27850/PDF"><meta data-n-head="ssr" name="DC.Creator" content="Lu"><meta data-n-head="ssr" name="DC.Contributor" content="Lu Xu"><meta data-n-head="ssr" name="DC.Contributor" content="Leslie Sanders"><meta data-n-head="ssr" name="DC.Contributor" content="Kay Li"><meta data-n-head="ssr" name="DC.Contributor" content="James C L Chow"><meta data-n-head="ssr" name="citation_authors" content="Lu Xu"><meta data-n-head="ssr" name="citation_authors" content="Leslie Sanders"><meta data-n-head="ssr" name="citation_authors" content="Kay Li"><meta data-n-head="ssr" name="citation_authors" content="James C L Chow"><title>JMIR Cancer - Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review</title><link data-n-head="ssr" rel="apple-touch-icon" sizes="57x57" href="https://asset.jmir.pub/assets/static/images/apple-touch-icon-57x57.png"><link data-n-head="ssr" rel="apple-touch-icon" sizes="114x114" 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Reviews on Innovations in Cancer (34) </a><a href="/themes/763" data-test="article-collection" aria-label="573 articles belongs to Chatbots and Conversational Agents e-collection/theme issue" class="collection__link"> Chatbots and Conversational Agents (573) </a><a href="/themes/500" data-test="article-collection" aria-label="1584 articles belongs to Machine Learning e-collection/theme issue" class="collection__link"> Machine Learning (1584) </a><a href="/themes/797" data-test="article-collection" aria-label="1547 articles belongs to Artificial Intelligence e-collection/theme issue" class="collection__link"> Artificial Intelligence (1547) </a><a href="/themes/521" data-test="article-collection" aria-label="58 articles belongs to Cancer Self-Management e-collection/theme issue" class="collection__link"> Cancer Self-Management (58) </a><a href="/themes/341" data-test="article-collection" aria-label="208 articles belongs to Emotional, Social, Psychological Support for Cancer 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fa-thumbtack"></span> <div><span class="ml-2"> Preprints (earlier versions) of this paper are available at <a data-test="preprint-link" aria-label="'Preprints (earlier versions) of this paper are available at preprints.jmir.org/preprint/'27850" href="https://preprints.jmir.org/preprint/27850" target="_blank">https://preprints.jmir.org/preprint/27850</a>, first published <time datetime="February 09, 2021">February 09, 2021</time>. </span></div></div></div> <div class="info mt-3"><div class="info__article-img"><div data-v-10f10a3e><img data-srcset="https://asset.jmir.pub/assets/1f6273f749f7b0fa0b4f42b4feaed538.png 480w,https://asset.jmir.pub/assets/1f6273f749f7b0fa0b4f42b4feaed538.png 960w,https://asset.jmir.pub/assets/1f6273f749f7b0fa0b4f42b4feaed538.png 1920w,https://asset.jmir.pub/assets/1f6273f749f7b0fa0b4f42b4feaed538.png 2500w" alt="Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review" title="Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review" aria-label="Article Thumbnail Image" src="https://asset.jmir.pub/placeholder.svg" data-v-10f10a3e></div> <div data-test="article-img-info" class="info__article-img-info"><span aria-hidden="true" class="icon fas fa-search-plus"></span></div></div> <div class="info__title-authors"><h1 tabindex="0" aria-label="Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review" class="h3 mb-0 mt-0">Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review</h1> <h2 class="info__hidden-title"> Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review </h2> <div class="mt-3"><p tabindex="0" class="authors-for-screen-reader"> Authors of this article: </p> <span data-test="authors-info" class="info__authors"><a href="/search?term=Lu%20Xu&type=author&precise=true" aria-label="Lu Xu. Search more articles by this author."> Lu Xu<sup>1, 2</sup> </a> <span><a aria-label="Visit this author on ORCID website" data-test="orcid-link" target="_blank" href="https://orcid.org/0000-0002-8485-9968"><img src="https://asset.jmir.pub/assets/static/images/Orcid-ID-Logo-Colour.png" alt="Author Orcid Image" aria-label="Author Orcid Image" class="info__orcid-img"></a></span> <span style="margin-left: -2px;"> ; </span></span><span data-test="authors-info" class="info__authors"><a href="/search?term=Leslie%20Sanders&type=author&precise=true" aria-label="Leslie Sanders. Search more articles by this author."> Leslie Sanders<sup>3</sup> </a> <span><a aria-label="Visit this author on ORCID website" data-test="orcid-link" target="_blank" href="https://orcid.org/0000-0002-6990-7722"><img src="https://asset.jmir.pub/assets/static/images/Orcid-ID-Logo-Colour.png" alt="Author Orcid Image" aria-label="Author Orcid Image" class="info__orcid-img"></a></span> <span style="margin-left: -2px;"> ; </span></span><span data-test="authors-info" class="info__authors"><a href="/search?term=Kay%20Li&type=author&precise=true" aria-label="Kay Li. Search more articles by this author."> Kay Li<sup>4</sup> </a> <span><a aria-label="Visit this author on ORCID website" data-test="orcid-link" target="_blank" href="https://orcid.org/0000-0002-5765-1635"><img src="https://asset.jmir.pub/assets/static/images/Orcid-ID-Logo-Colour.png" alt="Author Orcid Image" aria-label="Author Orcid Image" class="info__orcid-img"></a></span> <span style="margin-left: -2px;"> ; </span></span><span data-test="authors-info" class="info__authors"><a href="/search?term=James%20C%20L%20Chow&type=author&precise=true" aria-label="James C L Chow. Search more articles by this author."> James C L Chow<sup>5, 6</sup> </a> <span><a aria-label="Visit this author on ORCID website" data-test="orcid-link" target="_blank" href="https://orcid.org/0000-0003-4202-4855"><img src="https://asset.jmir.pub/assets/static/images/Orcid-ID-Logo-Colour.png" alt="Author Orcid Image" aria-label="Author Orcid Image" class="info__orcid-img"></a></span> </span></div> </div></div> <div role="tablist" aria-label="Article" class="tabs"><a href="/2021/4/e27850/" aria-current="page" role="tab" aria-label="Article" data-test="tabs" class="nuxt-link-exact-active nuxt-link-active active"> Article </a><a href="/2021/4/e27850/authors" role="tab" aria-label="Authors" data-test="tabs"> Authors </a><a href="/2021/4/e27850/citations" role="tab" aria-label="Cited by (240)" data-test="tabs"> Cited by (240) </a><a href="/2021/4/e27850/tweetations" role="tab" aria-label="Tweetations (13)" data-test="tabs"> Tweetations (13) </a><a href="/2021/4/e27850/metrics" role="tab" aria-label="Metrics" data-test="tabs"> Metrics </a></div> <div class="container"><div class="row"><div class="col-lg-3 mb-5 sidebar-sections"><div class="sidebar-nav"><div class="sidebar-nav-sticky"><ul></ul></div></div></div> <div data-test="keyword-links" class="col-lg-9 article"><main id="wrapper" class="wrapper ArticleMain clearfix"><section class="inner-wrapper clearfix"><section class="main-article-content clearfix"><article class="ajax-article-content"><h4 class="h4-original-paper"><span class="typcn typcn-document-text"></span>Review</h4><div class="authors-container"><div class="authors clearfix"></div></div><div class="authors-container"><div class="authors clearfix"></div></div><div class="authors-container"><div class="authors clearfix"><ul class="clearfix"><li><a href="/search/searchResult?field%5B%5D=author&criteria%5B%5D=Lu+Xu" class="btn-view-author-options">Lu Xu<sup><small>1,</small></sup><sup><small>2</small></sup>, MEng</a><a class="author-orcid" href="https://orcid.org/0000-0002-8485-9968" target="_blank" title="ORCID"> </a>; </li><li><a href="/search/searchResult?field%5B%5D=author&criteria%5B%5D=Leslie+Sanders" class="btn-view-author-options">Leslie Sanders<sup><small>3</small></sup>, PhD</a><a class="author-orcid" href="https://orcid.org/0000-0002-6990-7722" target="_blank" title="ORCID"> </a>; </li><li><a href="/search/searchResult?field%5B%5D=author&criteria%5B%5D=Kay+Li" class="btn-view-author-options">Kay Li<sup><small>4</small></sup>, PhD</a><a class="author-orcid" href="https://orcid.org/0000-0002-5765-1635" target="_blank" title="ORCID"> </a>; </li><li><a href="/search/searchResult?field%5B%5D=author&criteria%5B%5D=James%20C%20L+Chow" class="btn-view-author-options">James C L Chow<sup><small>5,</small></sup><sup><small>6</small></sup>, PhD</a><a class="author-orcid" href="https://orcid.org/0000-0003-4202-4855" target="_blank" title="ORCID"> </a></li></ul><div class="author-affiliation-details"><p><sup>1</sup>Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada</p><p><sup>2</sup>Department of Medical Biophysics, Western University, London, ON, Canada</p><p><sup>3</sup>Department of Humanities, York University, Toronto, ON, Canada</p><p><sup>4</sup>Department of English, York University, Toronto, ON, Canada</p><p><sup>5</sup>Department of Medical Physics, Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada</p><p><sup>6</sup>Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada</p></div></div><div class="corresponding-author-and-affiliations clearfix"><div class="corresponding-author-details"><h3>Corresponding Author:</h3><p>James C L Chow, PhD</p><p></p><p>Department of Medical Physics, Radiation Medicine Program</p><p>Princess Margaret Cancer Centre</p><p>University Health Network</p><p>7/F, 700 University Avenue</p><p>Toronto, ON, M5G 1X6</p><p>Canada</p><p>Phone: 1 9464501 ext 5089</p><p>Fax:1 9466566</p><p>Email: <a href="mailto:james.chow@rmp.uhn.ca">james.chow@rmp.uhn.ca</a></p><br></div></div></div><section class="article-content clearfix"><article class="abstract"><h3 id="Abstract" class="navigation-heading" data-label="Abstract">Abstract</h3><p><span class="abstract-sub-heading">Background: </span>Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility.<br></p><p><span class="abstract-sub-heading">Objective: </span>This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation.<br></p><p><span class="abstract-sub-heading">Methods: </span>A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion.<br></p><p><span class="abstract-sub-heading">Results: </span>Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored.<br></p><p><span class="abstract-sub-heading">Conclusions: </span>Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.<br></p><strong class="h4-article-volume-issue">JMIR Cancer 2021;7(4):e27850</strong><br><br><span class="article-doi"><a href="https://doi.org/10.2196/27850">doi:10.2196/27850</a></span><br><br><h3 class="h3-main-heading" id="Keywords">Keywords</h3><div class="keywords"><span><a href="/search?type=keyword&term=chatbot&precise=true">chatbot</a>; </span><span><a href="/search?type=keyword&term=artificial%20intelligence&precise=true">artificial intelligence</a>; </span><span><a href="/search?type=keyword&term=machine%20learning&precise=true">machine learning</a>; </span><span><a href="/search?type=keyword&term=health&precise=true">health</a>; </span><span><a href="/search?type=keyword&term=medicine&precise=true">medicine</a>; </span><span><a href="/search?type=keyword&term=communication&precise=true">communication</a>; </span><span><a href="/search?type=keyword&term=diagnosis&precise=true">diagnosis</a>; </span><span><a href="/search?type=keyword&term=cancer%20therapy&precise=true">cancer therapy</a>; </span><span><a href="/search?type=keyword&term=ethics&precise=true">ethics</a>; </span><span><a href="/search?type=keyword&term=medical%20biophysics&precise=true">medical biophysics</a>; </span><span><a href="/search?type=keyword&term=mobile%20phone&precise=true">mobile phone</a> </span></div><div id="trendmd-suggestions"></div></article><br><article class="main-article clearfix"><br><h3 class="navigation-heading h3-main-heading" id="Introduction" data-label="Introduction">Introduction</h3><h4>Background</h4><p class="abstract-paragraph">Artificial intelligence (AI) is at the forefront of transforming numerous aspects of our lives by modifying the way we analyze information and improving decision-making through problem solving, reasoning, and learning. Machine learning (ML) is a subset of AI that improves its performance based on the data provided to a generic algorithm from experience rather than defining rules in traditional approaches [<span class="footers"><a class="citation-link" href="#ref1" rel="footnote">1</a></span>]. Advancements in ML have provided benefits in terms of accuracy, decision-making, quick processing, cost-effectiveness, and handling of complex data [<span class="footers"><a class="citation-link" href="#ref2" rel="footnote">2</a></span>]. Chatbots, also known as chatter robots, smart bots, conversational agents, digital assistants, or intellectual agents, are prime examples of AI systems that have evolved from ML. The Oxford dictionary defines a chatbot as “a computer program that can hold a conversation with a person, usually over the internet<i>.</i>” They can also be physical entities designed to socially interact with humans or other robots. Predetermined responses are then generated by analyzing user input, on text or spoken ground, and accessing relevant knowledge [<span class="footers"><a class="citation-link" href="#ref3" rel="footnote">3</a></span>]. Problems arise when dealing with more complex situations in dynamic environments and managing social conversational practices according to specific contexts and unique communication strategies [<span class="footers"><a class="citation-link" href="#ref4" rel="footnote">4</a></span>].</p><p class="abstract-paragraph">Given these effectual benefits, it is not surprising that chatbots have rapidly evolved over the past 2 decades and integrated themselves into numerous fields, such as entertainment, travel, gaming, robotics, and security. Chatbots have been proven to be particularly applicable in various health care components that usually involve face-to-face interactions. With their ability for complex dialog management and conversational flexibility, integration of chatbot technology into clinical practice may reduce costs, refine workflow efficiencies, and improve patient outcomes [<span class="footers"><a class="citation-link" href="#ref5" rel="footnote">5</a></span>]. A web-based, self-report survey examining physicians’ perspectives found positive benefits of health care chatbots in managing one’s own health; for improved physical, psychological, and behavioral outcomes; and most notably, for administrative purposes [<span class="footers"><a class="citation-link" href="#ref6" rel="footnote">6</a></span>]. In light of the opportunities provided by this relatively new technology, potential limitations and areas of concern may arise that could potentially harm users. Concerns regarding accuracy, cybersecurity, lack of empathy, and technological maturity are reported as potential factors associated with the delay in chatbot acceptability or integration into health care [<span class="footers"><a class="citation-link" href="#ref7" rel="footnote">7</a></span>].</p><h4>Objectives</h4><p class="abstract-paragraph">This narrative review paper reports on health care components for chatbots, with a focus on cancer therapy. The rest of this paper is organized as follows: first, we introduce the developmental progress with a general overview of the architecture, design concepts, and types of chatbots; the main <i>Results</i> section focuses on the role that chatbots play in areas related to oncology, such as diagnosis, treatment, monitoring, support, workflow efficiency, and health promotion; and the <i>Discussion</i> section analyzes potential limitations and concerns for successful implementation while addressing future applications and research topics.</p><br><h3 class="navigation-heading h3-main-heading" id="Methods" data-label="Methods">Methods</h3><p class="abstract-paragraph">This review focuses on articles from peer-reviewed journals and conference proceedings. The following databases were searched from October to December 2020 for relevant and current studies from 2000 to 2020: IEEE Xplore, PubMed, Web of Science, Scopus, and OVID. The literature search used the following key terms: <i>chatbot</i>, <i>chatter robot</i>, <i>conversational agent</i>, <i>artificial intelligence</i>, and <i>machine learning</i>. For further refinement, these key terms were combined with more specific terms aligned with the focus of the paper. This included <i>healthcare</i>, <i>cancer therapy</i>, <i>oncology</i>, <i>diagnosis</i>, <i>treatment</i>, <i>radiation therapy</i>, and <i>radiotherapy</i>. The searches were not limited by language or study design. Letters and technical reports were excluded from the search. The full list of sources and search strategies is available from the authors.</p><p class="abstract-paragraph">The screening of chatbots was guided by a systematic review process from the Botlist directory during the period of January 2021. This directory was chosen as it was open-access and categorized the chatbots under many different categories (ie, health care, communication, and entertainment) and contained many commonly used messaging services (ie, Facebook Messenger, Discord, Slack, Kik, and Skype). A total of 78 chatbots were identified for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. It should be noted that using the health filters from a web directory limits the results to the search strategy and marketing label. Thus, the results from equivalent studies may differ when repeated.</p><br><h3 class="navigation-heading h3-main-heading" id="Results" data-label="Results">Results</h3><h4>Chatbot History and Evolution</h4><p class="abstract-paragraph">The idea of a chatbot was first introduced in 1950 when Alan Turing proposed the question, “Can machines think?” [<span class="footers"><a class="citation-link" href="#ref8" rel="footnote">8</a></span>]. The earliest forms were designed to pass the Turing test and mimic human conversations as much as possible. In 1966, ELIZA (MIT Artificial Intelligence Library) was the first known chatbot developed to act as a psychotherapist, using pattern matching and template-based responses to converse in a question-based format [<span class="footers"><a class="citation-link" href="#ref9" rel="footnote">9</a></span>]. Improvements were made to build a more human-like and personalized entity by incorporating a personality in PARRY (developed Kenneth Colby) that simulated a paranoid patient [<span class="footers"><a class="citation-link" href="#ref10" rel="footnote">10</a></span>]. One of the most well-known chatbots is ALICE, developed in 1995 by Richard Wallace, which uses a pattern-matching technique to retrieve example sentences from output templates and avoid inappropriate responses [<span class="footers"><a class="citation-link" href="#ref11" rel="footnote">11</a></span>]. A renewed interest in AI and advances in ML have led to the growing use and availability of chatbots in various fields [<span class="footers"><a class="citation-link" href="#ref12" rel="footnote">12</a></span>]. SmarterChild (ActiveBuddy, Inc) [<span class="footers"><a class="citation-link" href="#ref13" rel="footnote">13</a></span>] became widely accessible through messenger apps, followed by more familiar web-based assistants using voice-activated systems, such as Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana. On the basis of our analysis (<span class="footers"><a class="citation-link" href="#figure1" rel="footnote">Figure 1</a></span>), the most popular developments of chatbots for health care purposes are diagnostics, patient support (ie, mental health counseling), and health promotion. Some of these applications will be further explored in the following section for cancer applications.</p><figure><a name="figure1">&lrm;</a><a class="fancybox" title="Figure 1. Search and screening for health care chatbots. Chatbots using more than one platform are included." href="https://asset.jmir.pub/assets/0ecd31b048dfddf81d5d59d10f97c57a.png" id="figure1"><img class="figure-image" src="https://asset.jmir.pub/assets/0ecd31b048dfddf81d5d59d10f97c57a.png"></a><figcaption><span class="typcn typcn-image"></span>Figure 1. Search and screening for health care chatbots. Chatbots using more than one platform are included. </figcaption><a class="fancybox" href="https://asset.jmir.pub/assets/0ecd31b048dfddf81d5d59d10f97c57a.png" title="Figure 1. Search and screening for health care chatbots. Chatbots using more than one platform are included.">View this figure</a></figure><h4>Chatbot General Architecture</h4><p class="abstract-paragraph">Although there are a variety of techniques for the development of chatbots, the general layout is relatively straightforward. As a computer application that uses ML to mimic human conversation, the underlying concept is similar for all types with 4 essential stages (input processing, input understanding, response generation, and response selection) [<span class="footers"><a class="citation-link" href="#ref14" rel="footnote">14</a></span>]. A simplified general chatbot architecture is illustrated in <span class="footers"><a class="citation-link" href="#figure2" rel="footnote">Figure 2</a></span>. First, the user makes a request, in text or speech format, which is received and interpreted by the chatbot. From there, the processed information could be remembered, or more details could be requested for clarification. After the request is understood, the requested actions are performed, and the data of interest are retrieved from the database or external sources [<span class="footers"><a class="citation-link" href="#ref15" rel="footnote">15</a></span>].</p><figure><a name="figure2">&lrm;</a><a class="fancybox" title="Figure 2. Schematic representation of general chatbot architecture." href="https://asset.jmir.pub/assets/c59c57b737af6a17b92c8286720da480.png" id="figure2"><img class="figure-image" src="https://asset.jmir.pub/assets/c59c57b737af6a17b92c8286720da480.png"></a><figcaption><span class="typcn typcn-image"></span>Figure 2. Schematic representation of general chatbot architecture. </figcaption><a class="fancybox" href="https://asset.jmir.pub/assets/c59c57b737af6a17b92c8286720da480.png" title="Figure 2. Schematic representation of general chatbot architecture.">View this figure</a></figure><h4>Chatbot Types</h4><p class="abstract-paragraph">With the vast number of algorithms, tools, and platforms available, understanding the different types and end purposes of these chatbots will assist developers in choosing the optimal tools when designing them to fit the specific needs of users. These categories are not exclusive, as chatbots may possess multiple characteristics, making the process more variable. The 5 main types are described below [<span class="footers"><a class="citation-link" href="#ref15" rel="footnote">15</a></span>]. <span class="footers"><a class="citation-link" href="#box1" rel="footnote">Textbox 1</a></span> describes some examples of the recommended apps for each type of chatbot but are not limited to the ones specified.</p><p class="abstract-paragraph">Knowledge domain classification is based on accessible knowledge or the data used to train the chatbot. Under this category are the open domain for general topics and the closed domain focusing on more specific information. Service-provided classification is dependent on sentimental proximity to the user and the amount of intimate interaction dependent on the task performed. This can be further divided into interpersonal for providing services to transmit information, intrapersonal for companionship or personal support to humans, and interagent to communicate with other chatbots [<span class="footers"><a class="citation-link" href="#ref14" rel="footnote">14</a></span>]. The next classification is based on goals with the aim of achievement, subdivided into informative, conversational, and task based. Response generation chatbots, further classified as rule based, retrieval based, and generative, account for the process of analyzing inputs and generating responses [<span class="footers"><a class="citation-link" href="#ref16" rel="footnote">16</a></span>]. Finally, human-aided classification incorporates human computation, which provides more flexibility and robustness but lacks the speed to accommodate more requests [<span class="footers"><a class="citation-link" href="#ref17" rel="footnote">17</a></span>].</p><div class="textbox-container" id="box1"><h5>Recommended health care components for the different types of chatbots.</h5><p class="abstract-paragraph"><b>Knowledge domain</b></p><ul><li class="spacey">Open domain: responding to more general and broader topics that can be easily searched within databases; may be the preferred chatbot type for routine symptom screening, connecting to providers or services, or health promotion apps</li><li class="spacey">Closed domain: responding to complex or specific questions requiring more in-depth research; may be the preferred chatbot type for treatment planning or recommendation</li></ul><p class="abstract-paragraph"><b>Service provided</b></p><ul><li class="spacey">Interpersonal: used mainly to transmit information without much intimate connection with users; may be the preferred chatbot type for imaging diagnostics or hereditary assessment where the main duty is to relay factual information to users</li><li class="spacey">Intrapersonal: tailored for companionship or support; may be the preferred chatbot type for counseling, emotional support, or health promotion that requires a sense of human touch</li><li class="spacey">Interagent: used for communicating with other chatbots or computer systems; may be the preferred chatbot type for administration purposes when transferring patient information between locations</li></ul><p class="abstract-paragraph"><b>Goal based</b></p><ul><li class="spacey">Informative: designed to provide information from warehouse database or inventory entry; may be the preferred chatbot type for connecting patients with resources or remote patient monitoring</li><li class="spacey">Conversational: built with the purpose of conversing with users as naturally as possible; may be the preferred chatbot type for counseling, emotional support, or health promotion</li><li class="spacey">Task based: only performs 1 specific task where actions are predetermined; may be the preferred chatbot type for screening and diagnostics</li></ul><p class="abstract-paragraph"><b>Response generation</b></p><ul><li class="spacey">Uses pattern matching when the domain is narrow and sufficient data are available to train the system; may be the preferred chatbot type for screening and diagnostics</li></ul><p class="abstract-paragraph"><b>Human aided</b></p><ul><li class="spacey">Incorporates human computation that increases flexibility and robustness but decreases speed; may be the preferred chatbot type for most apps except for support or workflow efficiency, where speed is an essential factor in the delivery of care</li></ul><figcaption><span class="typcn typcn-image"></span>Textbox 1. Recommended health care components for the different types of chatbots.</figcaption></div><h4>Chatbots in Cancer Therapy</h4><h5>Overview</h5><p class="abstract-paragraph">Cancer has become a major health crisis and is the second leading cause of death in the United States [<span class="footers"><a class="citation-link" href="#ref18" rel="footnote">18</a></span>]. The exponentially increasing number of patients with cancer each year may be because of a combination of carcinogens in the environment and improved quality of care. The latter aspect could explain why cancer is slowly becoming a chronic disease that is manageable over time [<span class="footers"><a class="citation-link" href="#ref19" rel="footnote">19</a></span>]. Added life expectancy poses new challenges for both patients and the health care team. For example, many patients now require extended at-home support and monitoring, whereas health care workers deal with an increased workload. Although clinicians’ knowledge base in the use of scientific evidence to guide decision-making has expanded, there are still many other facets to the quality of care that has yet to catch up. Key areas of focus are safety, effectiveness, timeliness, efficiency, equitability, and patient-centered care [<span class="footers"><a class="citation-link" href="#ref20" rel="footnote">20</a></span>].</p><p class="abstract-paragraph">Chatbots have the potential to address many of the current concerns regarding cancer care mentioned above. This includes the triple aim of health care that encompasses improving the experience of care, improving the health of populations, and reducing per capita costs [<span class="footers"><a class="citation-link" href="#ref21" rel="footnote">21</a></span>]. Chatbots can improve the quality or experience of care by providing efficient, equitable, and personalized medical services. We can think of them as intermediaries between physicians for facilitating the history taking of sensitive and intimate information before consultations. They could also be thought of as decision aids that deliver regular feedback on disease progression and treatment reactions to help clinicians better understand individual conditions. Preventative measures of cancer have become a priority worldwide, as early detection and treatment alone have not been effective in eliminating this disease [<span class="footers"><a class="citation-link" href="#ref22" rel="footnote">22</a></span>]. Physical, psychological, and behavioral improvements of underserved or vulnerable populations may even be possible through chatbots, as they are so readily accessible through common messaging platforms. Health promotion use, such as lifestyle coaching, healthy eating, and smoking cessation, has been one of the most common chatbots according to our search. In addition, chatbots could help save a significant amount of health care costs and resources. Newer therapeutic innovations have come with a heavy price tag, and out-of-pocket expenses have placed a significant strain on patients’ financial well-being [<span class="footers"><a class="citation-link" href="#ref23" rel="footnote">23</a></span>]. With chatbots implemented in cancer care, consultations for minor health concerns may be avoided, which allows clinicians to spend more time with patients who need their attention the most. Costs may also be reduced by delivering medical services more efficiently. For example, the workflow can be streamlined by assisting physicians in administrative tasks, such as scheduling appointments, providing medical information, or locating clinics.</p><p class="abstract-paragraph">With the rapidly increasing applications of chatbots in health care, this section will explore several areas of development and innovation in cancer care. Various examples of current chatbots provided below will illustrate their ability to tackle the triple aim of health care. The specific use case of chatbots in oncology with examples of actual products and proposed designs are outlined in <span class="footers"><a class="citation-link" href="#table1" rel="footnote">Table 1</a></span>.</p><div class="figure-table"><figcaption><span class="typcn typcn-clipboard"></span>Table 1. Use case for chatbots in oncology, with examples of current specific applications or proposed designs.</figcaption><table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides"><col width="30" span="1"><col width="30" span="1"><col width="300" span="1"><col width="0" span="1"><col width="640" span="1"><thead><tr valign="top"><td colspan="4" rowspan="1">Use case and application, chatbot</td><td rowspan="1" colspan="1">Function</td></tr></thead><tbody><tr valign="top"><td colspan="5" rowspan="1"><b>Screening and diagnosis</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Imaging diagnostic</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Medical Sieve [<span class="footers"><a class="citation-link" href="#ref24" rel="footnote">24</a></span>]</td><td colspan="2" rowspan="1">Examines radiological images to aid clinicians with diagnosis</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Symptom screening</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Quro [<span class="footers"><a class="citation-link" href="#ref25" rel="footnote">25</a></span>]</td><td colspan="2" rowspan="1">Presynopsis based on symptoms and history to predict user conditions</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Buoy Health [<span class="footers"><a class="citation-link" href="#ref26" rel="footnote">26</a></span>]</td><td colspan="2" rowspan="1">Assists in identifying the cause of illnesses and provides medical advice</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Harshitha breast cancer screening [<span class="footers"><a class="citation-link" href="#ref27" rel="footnote">27</a></span>]</td><td colspan="2" rowspan="1">Dialog flow to give an initial analysis of breast cancer symptoms</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Babylon [<span class="footers"><a class="citation-link" href="#ref28" rel="footnote">28</a></span>]</td><td colspan="2" rowspan="1">Symptom checker</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Your.md [<span class="footers"><a class="citation-link" href="#ref28" rel="footnote">28</a></span>]</td><td colspan="2" rowspan="1">Symptom checker</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Ada [<span class="footers"><a class="citation-link" href="#ref28" rel="footnote">28</a></span>]</td><td colspan="2" rowspan="1">Symptom checker</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Hereditary assessment</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">ItRuns [<span class="footers"><a class="citation-link" href="#ref29" rel="footnote">29</a></span>]</td><td colspan="2" rowspan="1">Gathers family history information at the population level to determine the risk of hereditary cancer</td></tr><tr valign="top"><td colspan="5" rowspan="1"><b>Treatment</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Patient treatment recommendation</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Mathew [<span class="footers"><a class="citation-link" href="#ref30" rel="footnote">30</a></span>]</td><td colspan="2" rowspan="1">Identifies symptoms, predicts the disease using a symptom–disease data set, and recommends a suitable treatment</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Madhu [<span class="footers"><a class="citation-link" href="#ref31" rel="footnote">31</a></span>]</td><td colspan="2" rowspan="1">Provides a list of available treatments for various diseases and informs the user of the composition and prescribed use of the medications</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Connecting patients with providers or resources</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Divya [<span class="footers"><a class="citation-link" href="#ref32" rel="footnote">32</a></span>]</td><td colspan="2" rowspan="1">Engages patients regarding their symptoms to provide a personalized diagnosis and connects with appropriate medical service</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Rarhi [<span class="footers"><a class="citation-link" href="#ref33" rel="footnote">33</a></span>]</td><td colspan="2" rowspan="1">Provides a diagnosis based on symptoms, measures the seriousness, and connects with a physician</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="2" rowspan="1"><b>Physician treatment planning</b></td><td colspan="2" rowspan="1"><br></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Watson for Oncology [<span class="footers"><a class="citation-link" href="#ref34" rel="footnote">34</a></span>]</td><td colspan="2" rowspan="1">Examines data from records and medical notes to generate an evidence-based treatment plan for oncologists</td></tr><tr valign="top"><td colspan="5" rowspan="1"><b>Monitoring</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Remote patient monitoring</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">STREAMD [<span class="footers"><a class="citation-link" href="#ref35" rel="footnote">35</a></span>]</td><td colspan="2" rowspan="1">Provides access to care instructions and educational information</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Conversa [<span class="footers"><a class="citation-link" href="#ref35" rel="footnote">35</a></span>]</td><td colspan="2" rowspan="1">Provides access to care instructions and educational information</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Memora Health [<span class="footers"><a class="citation-link" href="#ref35" rel="footnote">35</a></span>]</td><td colspan="2" rowspan="1">Provides access to care instructions and educational information</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">AiCure [<span class="footers"><a class="citation-link" href="#ref36" rel="footnote">36</a></span>]</td><td colspan="2" rowspan="1">Coaches patients to manage their condition and adhere to instructions</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Infinity [<span class="footers"><a class="citation-link" href="#ref37" rel="footnote">37</a></span>]</td><td colspan="2" rowspan="1">Assesses health outcomes and impact of phone-based monitoring for patients with cancer aged ≥65 years</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Vik [<span class="footers"><a class="citation-link" href="#ref38" rel="footnote">38</a></span>,<span class="footers"><a class="citation-link" href="#ref39" rel="footnote">39</a></span>]</td><td colspan="2" rowspan="1">Addresses patients’ daily needs and concerns</td></tr><tr valign="top"><td colspan="5" rowspan="1"><b>Support</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Counseling</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Vivobot [<span class="footers"><a class="citation-link" href="#ref40" rel="footnote">40</a></span>]</td><td colspan="2" rowspan="1">Cognitive and behavioral intervention for positive psychology skills and promoting well-being</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Emotional support</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Youper [<span class="footers"><a class="citation-link" href="#ref26" rel="footnote">26</a></span>]</td><td colspan="2" rowspan="1">Daily emotional support and mental health tracking</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Wysa [<span class="footers"><a class="citation-link" href="#ref26" rel="footnote">26</a></span>]</td><td colspan="2" rowspan="1">Daily emotional support and mental health tracking</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Replika [<span class="footers"><a class="citation-link" href="#ref26" rel="footnote">26</a></span>]</td><td colspan="2" rowspan="1">Daily emotional support and mental health tracking</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Unmind [<span class="footers"><a class="citation-link" href="#ref26" rel="footnote">26</a></span>]</td><td colspan="2" rowspan="1">Daily emotional support and mental health tracking</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Shim [<span class="footers"><a class="citation-link" href="#ref26" rel="footnote">26</a></span>]</td><td colspan="2" rowspan="1">Daily emotional support and mental health tracking</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Woebot [<span class="footers"><a class="citation-link" href="#ref41" rel="footnote">41</a></span>]</td><td colspan="2" rowspan="1">Daily emotional support and mental health tracking</td></tr><tr valign="top"><td colspan="5" rowspan="1"><b>Workflow efficiency</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Administration</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Sense.ly [<span class="footers"><a class="citation-link" href="#ref42" rel="footnote">42</a></span>]</td><td colspan="2" rowspan="1">Assists in monitoring appointments, manages patients’ conditions, and suggests therapies</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Careskore [<span class="footers"><a class="citation-link" href="#ref42" rel="footnote">42</a></span>]</td><td colspan="2" rowspan="1">Tracks vitals and anticipates the need for hospital admissions</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Mandy [<span class="footers"><a class="citation-link" href="#ref43" rel="footnote">43</a></span>]</td><td colspan="2" rowspan="1">Assists health care staff by automating the patient intake process</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Patient encounter</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">HOLMeS [<span class="footers"><a class="citation-link" href="#ref44" rel="footnote">44</a></span>]</td><td colspan="2" rowspan="1">Supports diagnosis, chooses the proper treatment pathway, and provides prevention check-ups</td></tr><tr valign="top"><td colspan="5" rowspan="1"><b>Health promotion</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>General lifestyle coaching</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">SWITCHes [<span class="footers"><a class="citation-link" href="#ref45" rel="footnote">45</a></span>]</td><td colspan="2" rowspan="1">Tracks patients’ progress, provides insight to physicians, and suggests suitable activities</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">CoachAI [<span class="footers"><a class="citation-link" href="#ref46" rel="footnote">46</a></span>]</td><td colspan="2" rowspan="1">Tracks patients’ progress, provides insight to physicians, and suggests suitable activities</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">WeightMentor [<span class="footers"><a class="citation-link" href="#ref47" rel="footnote">47</a></span>]</td><td colspan="2" rowspan="1">Provides self-help motivation for weight loss maintenance and allows for open conversation</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Healthy eating</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Health Hero [<span class="footers"><a class="citation-link" href="#ref48" rel="footnote">48</a></span>]</td><td colspan="2" rowspan="1">Guides in making informed decisions around food choices to change unhealthy eating habits</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Tasteful Bot [<span class="footers"><a class="citation-link" href="#ref48" rel="footnote">48</a></span>]</td><td colspan="2" rowspan="1">Guides in making informed decisions around food choices to change unhealthy eating habits</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Forksy [<span class="footers"><a class="citation-link" href="#ref48" rel="footnote">48</a></span>]</td><td colspan="2" rowspan="1">Guides in making informed decisions around food choices to change unhealthy eating habits</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">SLOWbot [<span class="footers"><a class="citation-link" href="#ref49" rel="footnote">49</a></span>]</td><td colspan="2" rowspan="1">Guides in making informed decisions around food choices to change unhealthy eating habits</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td colspan="4" rowspan="1"><b>Smoking cessation</b></td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">SMAG [<span class="footers"><a class="citation-link" href="#ref50" rel="footnote">50</a></span>]</td><td colspan="2" rowspan="1">Cognitive behavioral therapy</td></tr><tr valign="top"><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1"><br></td><td rowspan="1" colspan="1">Bella [<span class="footers"><a class="citation-link" href="#ref51" rel="footnote">51</a></span>]</td><td colspan="2" rowspan="1">Coaches to help quit smoking</td></tr></tbody></table></div><h5>Diagnostics and Screening</h5><p class="abstract-paragraph">An accurate diagnosis is critical for appropriate care to be administered. In terms of cancer diagnostics, AI-based computer vision is a function often used in chatbots that can recognize subtle patterns from images. This would increase physicians’ confidence when identifying cancer types, as even highly trained individuals may not always agree on the diagnosis [<span class="footers"><a class="citation-link" href="#ref52" rel="footnote">52</a></span>]. Studies have shown that the interpretation of medical images for the diagnosis of tumors performs equally well or better with AI compared with experts [<span class="footers"><a class="citation-link" href="#ref53" rel="footnote">53</a></span>-<span class="footers"><a class="citation-link" href="#ref56" rel="footnote">56</a></span>]. In addition, automated diagnosis may be useful when there are not enough specialists to review the images. This was made possible through deep learning algorithms in combination with the increasing availability of databases for the tasks of detection, segmentation, and classification [<span class="footers"><a class="citation-link" href="#ref57" rel="footnote">57</a></span>]. For example, Medical Sieve (IBM Corp) is a chatbot that examines radiological images to aid and communicate with cardiologists and radiologists to identify issues quickly and reliably [<span class="footers"><a class="citation-link" href="#ref24" rel="footnote">24</a></span>]. Similarly, InnerEye (Microsoft Corp) is a computer-assisted image diagnostic chatbot that recognizes cancers and diseases within the eye but does not directly interact with the user like a chatbot [<span class="footers"><a class="citation-link" href="#ref42" rel="footnote">42</a></span>]. Even with the rapid advancements of AI in cancer imaging, a major issue is the lack of a gold standard [<span class="footers"><a class="citation-link" href="#ref58" rel="footnote">58</a></span>].</p><p class="abstract-paragraph">From the patient’s perspective, various chatbots have been designed for symptom screening and self-diagnosis. The ability of patients to be directed to urgent referral pathways through early warning signs has been a promising market. Decreased wait times in accessing health care services have been found to correlate with improved patient outcomes and satisfaction [<span class="footers"><a class="citation-link" href="#ref59" rel="footnote">59</a></span>-<span class="footers"><a class="citation-link" href="#ref61" rel="footnote">61</a></span>]. The automated chatbot, Quro (Quro Medical, Inc), provides presynopsis based on symptoms and history to predict user conditions (average precision approximately 0.82) without a form-based data entry system [<span class="footers"><a class="citation-link" href="#ref25" rel="footnote">25</a></span>]. In addition to diagnosis, Buoy Health (Buoy Health, Inc) assists users in identifying the cause of their illness and provides medical advice [<span class="footers"><a class="citation-link" href="#ref26" rel="footnote">26</a></span>]. Another chatbot designed by Harshitha et al [<span class="footers"><a class="citation-link" href="#ref27" rel="footnote">27</a></span>] uses dialog flow to provide an initial analysis of breast cancer symptoms. It has been proven to be 95% accurate in differentiating between normal and cancerous images. Even with promising results, there are still potential areas for improvement. A study of 3 mobile app–based chatbot symptom checkers, Babylon (Babylon Health, Inc), Your.md (Healthily, Inc), and Ada (Ada, Inc), indicated that sensitivity remained low at 33% for the detection of head and neck cancer [<span class="footers"><a class="citation-link" href="#ref28" rel="footnote">28</a></span>]. The number of studies assessing the development, implementation, and effectiveness are still relatively limited compared with the diversity of chatbots currently available. Further studies are required to establish the efficacy across various conditions and populations. Nonetheless, chatbots for self-diagnosis are an effective way of advising patients as the first point of contact if accuracy and sensitivity requirements can be satisfied.</p><p class="abstract-paragraph">Early cancer detection can lead to higher survival rates and improved quality of life. Inherited factors are present in 5% to 10% of cancers, including breast, colorectal, prostate, and rare tumor syndromes [<span class="footers"><a class="citation-link" href="#ref62" rel="footnote">62</a></span>]. Family history collection is a proven way of easily accessing the genetic disposition of developing cancer to inform risk-stratified decision-making, clinical decisions, and cancer prevention [<span class="footers"><a class="citation-link" href="#ref63" rel="footnote">63</a></span>]. The web-based chatbot ItRuns (ItRunsInMyFamily) gathers family history information at the population level to determine the risk of hereditary cancer [<span class="footers"><a class="citation-link" href="#ref29" rel="footnote">29</a></span>]. We have yet to find a chatbot that incorporates deep learning to process large and complex data sets at a cellular level. Although not able to directly converse with users, DeepTarget [<span class="footers"><a class="citation-link" href="#ref64" rel="footnote">64</a></span>] and deepMirGene [<span class="footers"><a class="citation-link" href="#ref65" rel="footnote">65</a></span>] are capable of performing miRNA and target predictions using expression data with higher accuracy compared with non–deep learning models. With the advent of phenotype–genotype predictions, chatbots for genetic screening would greatly benefit from image recognition. New screening biomarkers are also being discovered at a rapid speed, so continual integration and algorithm training are required. These findings align with studies that demonstrate that chatbots have the potential to improve user experience and accessibility and provide accurate data collection [<span class="footers"><a class="citation-link" href="#ref66" rel="footnote">66</a></span>].</p><h5>Treatment</h5><p class="abstract-paragraph">Chatbots are now able to provide patients with treatment and medication information after diagnosis without having to directly contact a physician. Such a system was proposed by Mathew et al [<span class="footers"><a class="citation-link" href="#ref30" rel="footnote">30</a></span>] that identifies the symptoms, predicts the disease using a symptom–disease data set, and recommends a suitable treatment. Although this may seem as an attractive option for patients looking for a fast solution, computers are still prone to errors, and bypassing professional inspection may be an area of concern. Chatbots may also be an effective resource for patients who want to learn why a certain treatment is necessary. Madhu et al [<span class="footers"><a class="citation-link" href="#ref31" rel="footnote">31</a></span>] proposed an interactive chatbot app that provides a list of available treatments for various diseases, including cancer. This system also informs the user of the composition and prescribed use of medications to help select the best course of action. The diagnosis and course of treatment for cancer are complex, so a more realistic system would be a chatbot used to connect users with appropriate specialists or resources. A text-to-text chatbot by Divya et al [<span class="footers"><a class="citation-link" href="#ref32" rel="footnote">32</a></span>] engages patients regarding their medical symptoms to provide a personalized diagnosis and connects the user with the appropriate physician if major diseases are detected. Rarhi et al [<span class="footers"><a class="citation-link" href="#ref33" rel="footnote">33</a></span>] proposed a similar design that provides a diagnosis based on symptoms, measures the seriousness, and connects users with a physician if needed [<span class="footers"><a class="citation-link" href="#ref33" rel="footnote">33</a></span>]. In general, these systems may greatly help individuals in conducting daily check-ups, increase awareness of their health status, and encourage users to seek medical assistance for early intervention.</p><p class="abstract-paragraph">Chatbots have also been used by physicians during treatment planning. For example, IBM’s Watson for Oncology examines data from records and medical notes to generate an evidence-based treatment plan for oncologists [<span class="footers"><a class="citation-link" href="#ref34" rel="footnote">34</a></span>]. Studies have shown that Watson for Oncology still cannot replace experts at this moment, as quite a few cases are not consistent with experts (approximately 73% concordant) [<span class="footers"><a class="citation-link" href="#ref67" rel="footnote">67</a></span>,<span class="footers"><a class="citation-link" href="#ref68" rel="footnote">68</a></span>]. Nonetheless, this could be an effective decision-making tool for cancer therapy to standardize treatments. Although not specifically an oncology app, another chatbot example for clinicians’ use is the chatbot Safedrugbot (Safe In Breastfeeding) [<span class="footers"><a class="citation-link" href="#ref69" rel="footnote">69</a></span>]. This is a chat messaging service for health professionals offering assistance with appropriate drug use information during breastfeeding. Promising progress has also been made in using AI for radiotherapy to reduce the workload of radiation staff or identify at-risk patients by collecting outcomes before and after treatment [<span class="footers"><a class="citation-link" href="#ref70" rel="footnote">70</a></span>]. An ideal chatbot for health care professionals’ use would be able to accurately detect diseases and provide the proper course of recommendations, which are functions currently limited by time and budgetary constraints. Continual algorithm training and updates would be necessary because of the constant improvements in current standards of care. Further refinements and testing for the accuracy of algorithms are required before clinical implementation [<span class="footers"><a class="citation-link" href="#ref71" rel="footnote">71</a></span>]. This area holds tremendous potential, as an estimated ≥50% of all patients with cancer have used radiotherapy during the course of their treatment.</p><h5>Patient Monitoring</h5><p class="abstract-paragraph">Chatbots have been implemented in remote patient monitoring for postoperative care and follow-ups. The health care sector is among the most overwhelmed by those needing continued support outside hospital settings, as most patients newly diagnosed with cancer are aged ≥65 years [<span class="footers"><a class="citation-link" href="#ref72" rel="footnote">72</a></span>]. The integration of this application would improve patients’ quality of life and relieve the burden on health care providers through better disease management, reducing the cost of visits and allowing timely follow-ups. In terms of cancer therapy, remote monitoring can support patients by enabling higher dose chemotherapy drug delivery, reducing secondary hospitalizations, and providing health benefits after surgery [<span class="footers"><a class="citation-link" href="#ref73" rel="footnote">73</a></span>-<span class="footers"><a class="citation-link" href="#ref75" rel="footnote">75</a></span>].</p><p class="abstract-paragraph">StreamMD (StreamMD, Inc), Conversa (Conversa Health, Inc), and Memora Health (Memora Health, Inc) are chatbots that function on existing messaging platforms that provide patients with immediate access to care instructions and educational information [<span class="footers"><a class="citation-link" href="#ref35" rel="footnote">35</a></span>]. To ensure that patients adhere to instructions, AiCure (AiCure, Inc) uses a smartphone webcam to coach them in managing their condition. Recently, a chatbot architecture was proposed for patient support based on microservices to provide personalized eHealth functionalities and data storage [<span class="footers"><a class="citation-link" href="#ref36" rel="footnote">36</a></span>]. Several studies have supported the application of chatbots for patient monitoring [<span class="footers"><a class="citation-link" href="#ref76" rel="footnote">76</a></span>]. The semiautomized messaging chatbot Infinity (Facebook, Inc) was used to assess the health outcomes and health care impacts of phone-based monitoring for patients with cancer aged ≥65 years. After 2 years of implementation, there was a 97% satisfactory rate, and 87% considered monitoring useful, with the most reported benefit being treatment management and moral support [<span class="footers"><a class="citation-link" href="#ref37" rel="footnote">37</a></span>]. Similar results were discovered in 2 studies using Vik (WeFight, Inc), a text-based chatbot that responds to the daily needs and concerns of patients and their relatives with personal insights. A 1-year prospective study of 4737 patients with breast cancer reported a 94% overall satisfaction rate [<span class="footers"><a class="citation-link" href="#ref38" rel="footnote">38</a></span>]. A more in-depth analysis of the 132,970 messages showed that users were more likely to answer multiple-choice questions compared with open-ended ones, chatbots improved treatment compliance rate by >20% (<i>P</i>=.04), and intimate or sensitive topics were openly discussed. An area of concern is that retention rates drastically decreased to 31% by the end of this study. The other study was a phase 3, blind, noninferiority randomized controlled trial (n=132) to assess the level of patient satisfaction with the answers provided by chatbots versus those by physicians [<span class="footers"><a class="citation-link" href="#ref39" rel="footnote">39</a></span>]. Using 12 frequently asked questions on breast cancer, participants were split into 2 groups to rate the quality of answers from chatbots or physicians. Among patients with breast cancer in treatment or remission, chatbot answers were shown to be noninferior (<i>P</i><.001), with a success rate of 69% compared with 64% in the physician groups. Concerns regarding the chatbot’s ability to successfully answer more complex questions or detect differences between major and minor symptoms still remain to be addressed.</p><p class="abstract-paragraph">Further refinements and large-scale implementations are still required to determine the benefits across different populations and sectors in health care [<span class="footers"><a class="citation-link" href="#ref26" rel="footnote">26</a></span>]. Although overall satisfaction is found to be relatively high, there is still room for improvement by taking into account user feedback tailored to the patient’s changing needs during recovery. In combination with wearable technology and affordable software, chatbots have great potential to affect patient monitoring solutions.</p><h5>Patient Support</h5><p class="abstract-paragraph">The prevalence of cancer is increasing along with the number of survivors of cancer, partly because of improved treatment techniques and early detection [<span class="footers"><a class="citation-link" href="#ref77" rel="footnote">77</a></span>]. These individuals experience added health problems, such as infections, chronic diseases, psychological issues, and sleep disturbances, which often require specific needs that are not met by many practitioners (ie, medical, psychosocial, informational, and proactive contact) [<span class="footers"><a class="citation-link" href="#ref78" rel="footnote">78</a></span>]. A number of these individuals require support after hospitalization or treatment periods. Maintaining autonomy and living in a self-sustaining way within their home environment is especially important for older populations [<span class="footers"><a class="citation-link" href="#ref79" rel="footnote">79</a></span>]. Implementation of chatbots may address some of these concerns, such as reducing the burden on the health care system and supporting independent living.</p><p class="abstract-paragraph">With psychiatric disorders affecting at least 35% of patients with cancer, comprehensive cancer care now includes psychosocial support to reduce distress and foster a better quality of life [<span class="footers"><a class="citation-link" href="#ref80" rel="footnote">80</a></span>]. The first chatbot was designed for individuals with psychological issues [<span class="footers"><a class="citation-link" href="#ref9" rel="footnote">9</a></span>]; however, they continue to be used for emotional support and psychiatric counseling with their ability to express sympathy and empathy [<span class="footers"><a class="citation-link" href="#ref81" rel="footnote">81</a></span>]. Health-based chatbots delivered through mobile apps, such as Woebot (Woebot Health, Inc), Youper (Youper, Inc), Wysa (Wysa, Ltd), Replika (Luka, Inc), Unmind (Unmind, Inc), and Shim (Shim, Inc), offer daily emotional support and mental health tracking [<span class="footers"><a class="citation-link" href="#ref26" rel="footnote">26</a></span>]. A study performed on Woebot, developed based on cognitive behavioral therapy, showed that depressive symptoms were significantly reduced, and participants were more receptive than in traditional therapies [<span class="footers"><a class="citation-link" href="#ref41" rel="footnote">41</a></span>]. This agreed with the Shim results, also using the same type of therapy, which showed that the intervention was highly engaging, improved well-being, and reduced stress [<span class="footers"><a class="citation-link" href="#ref82" rel="footnote">82</a></span>]. When another chatbot was developed based on the structured association technique counseling method, the user’s motivation was enhanced, and stress was reduced [<span class="footers"><a class="citation-link" href="#ref83" rel="footnote">83</a></span>]. Similarly, a graph-based chatbot has been proposed to identify the mood of users through sentimental analysis and provide human-like responses to comfort patients [<span class="footers"><a class="citation-link" href="#ref84" rel="footnote">84</a></span>]. Vivobot (HopeLab, Inc) provides cognitive and behavioral interventions to deliver positive psychology skills and promote well-being. This psychiatric counseling chatbot was effective in engaging users and reducing anxiety in young adults after cancer treatment [<span class="footers"><a class="citation-link" href="#ref40" rel="footnote">40</a></span>]. The limitation to the abovementioned studies was that most participants were young adults, most likely because of the platform on which the chatbots were available. In addition, longer follow-up periods with larger and more diverse sample sizes are needed for future studies. Chatbots used for psychological support hold great potential, as individuals are more comfortable disclosing personal information when no judgments are formed, even if users could still discriminate their responses from that of humans [<span class="footers"><a class="citation-link" href="#ref82" rel="footnote">82</a></span>,<span class="footers"><a class="citation-link" href="#ref85" rel="footnote">85</a></span>].</p><h5>Workflow Efficiency</h5><p class="abstract-paragraph">Electronic health records have improved data availability but also increased the complexity of the clinical workflow, contributing to ineffective treatment plans and uninformed management [<span class="footers"><a class="citation-link" href="#ref86" rel="footnote">86</a></span>]. A streamlined process using ML techniques would allow clinicians to spend more time with patients by decreasing the time spent on data entry through the ease of documentation, exposing relevant patient information from the chart, automatically authorizing payment, or reducing medical errors [<span class="footers"><a class="citation-link" href="#ref58" rel="footnote">58</a></span>]. For example, Mandy is a chatbot that assists health care staff by automating the patient intake process [<span class="footers"><a class="citation-link" href="#ref43" rel="footnote">43</a></span>]. Using a combination of data-driven natural language processing with knowledge-driven diagnostics, this chatbot interviews the patient, understands their chief complaints, and submits reports to physicians for further analysis [<span class="footers"><a class="citation-link" href="#ref43" rel="footnote">43</a></span>]. Similarly, Sense.ly (Sense.ly, Inc) acts as a web-based nurse to assist in monitoring appointments, managing patients’ conditions, and suggesting therapies. Another chatbot that reduces the burden on clinicians and decreases wait time is Careskore (CareShore, Inc), which tracks vitals and anticipates the need for hospital admissions [<span class="footers"><a class="citation-link" href="#ref42" rel="footnote">42</a></span>]. Chatbots have also been proposed to autonomize patient encounters through several advanced eHealth services. In addition to collecting data and providing bookings, Health OnLine Medical Suggestions or HOLMES (Wipro, Inc) interacts with patients to support diagnosis, choose the proper treatment pathway, and provide prevention check-ups [<span class="footers"><a class="citation-link" href="#ref44" rel="footnote">44</a></span>]. Although the use of chatbots in health care and cancer therapy has the potential to enhance clinician efficiency, reimbursement codes for practitioners are still lacking before universal implementation. In addition, studies will need to be conducted to validate the effectiveness of chatbots in streamlining workflow for different health care settings. Nonetheless, chatbots hold great potential to complement telemedicine by streamlining medical administration and autonomizing patient encounters.</p><h5>Health Promotion</h5><p class="abstract-paragraph">Survivors of cancer, particularly those who underwent treatment during childhood, are more susceptible to adverse health risks and medical complications. Consequently, promoting a healthy lifestyle early on is imperative to maintain quality of life, reduce mortality, and decrease the risk of secondary cancers [<span class="footers"><a class="citation-link" href="#ref87" rel="footnote">87</a></span>]. According to the analysis from the web directory, health promotion chatbots are the most commonly available; however, most of them are only available on a single platform. Thus, interoperability on multiple common platforms is essential for adoption by various types of users across different age groups. In addition, voice and image recognition should also be considered, as most chatbots are still text based.</p><p class="abstract-paragraph">Healthy diets and weight control are key to successful disease management, as obesity is a significant risk factor for chronic conditions. Chatbots have been incorporated into health coaching systems to address health behavior modifications. For example, CoachAI and Smart Wireless Interactive Health System used chatbot technology to track patients’ progress, provide insight to physicians, and suggest suitable activities [<span class="footers"><a class="citation-link" href="#ref45" rel="footnote">45</a></span>,<span class="footers"><a class="citation-link" href="#ref46" rel="footnote">46</a></span>]. Another app is Weight Mentor, which provides self-help motivation for weight loss maintenance and allows for open conversation without being affected by emotions [<span class="footers"><a class="citation-link" href="#ref47" rel="footnote">47</a></span>]. Health Hero (Health Hero, Inc), Tasteful Bot (Facebook, Inc), Forksy (Facebook, Inc), and SLOWbot (iaso heath, Inc) guide users to make informed decisions on food choices to change unhealthy eating habits [<span class="footers"><a class="citation-link" href="#ref48" rel="footnote">48</a></span>,<span class="footers"><a class="citation-link" href="#ref49" rel="footnote">49</a></span>]. The effectiveness of these apps cannot be concluded, as a more rigorous analysis of the development, evaluation, and implementation is required. Nevertheless, chatbots are emerging as a solution for healthy lifestyle promotion through access and human-like communication while maintaining anonymity.</p><p class="abstract-paragraph">Most would assume that survivors of cancer would be more inclined to practice health protection behaviors with extra guidance from health professionals; however, the results have been surprising. Smoking accounts for at least 30% of all cancer deaths; however, up to 50% of survivors continue to smoke [<span class="footers"><a class="citation-link" href="#ref88" rel="footnote">88</a></span>]. The benefit of using chatbots for smoking cessation across various age groups has been highlighted in numerous studies showing improved motivation, accessibility, and adherence to treatment, which have led to increased smoking abstinence [<span class="footers"><a class="citation-link" href="#ref89" rel="footnote">89</a></span>-<span class="footers"><a class="citation-link" href="#ref91" rel="footnote">91</a></span>]. The cognitive behavioral therapy–based chatbot SMAG, supporting users over the Facebook social network, resulted in a 10% higher cessation rate compared with control groups [<span class="footers"><a class="citation-link" href="#ref50" rel="footnote">50</a></span>]. Motivational interview–based chatbots have been proposed with promising results, where a significant number of patients showed an increase in their confidence and readiness to quit smoking after 1 week [<span class="footers"><a class="citation-link" href="#ref92" rel="footnote">92</a></span>]. No studies have been found to assess the effectiveness of chatbots for smoking cessation in terms of ethnic, racial, geographic, or socioeconomic status differences. Creating chatbots with prespecified answers is simple; however, the problem becomes more complex when answers are open. Bella, one of the most advanced text-based chatbots on the market advertised as a coach for adults, gets stuck when responses are not prompted [<span class="footers"><a class="citation-link" href="#ref51" rel="footnote">51</a></span>]. Therefore, the reaction to unexpected responses is still an area in progress. Given all the uncertainties, chatbots hold potential for those looking to quit smoking, as they prove to be more acceptable for users when dealing with stigmatized health issues compared with general practitioners [<span class="footers"><a class="citation-link" href="#ref7" rel="footnote">7</a></span>].</p><br><h3 class="navigation-heading h3-main-heading" id="Discussion" data-label="Discussion">Discussion</h3><h4>Challenges and Limitations</h4><p class="abstract-paragraph">AI and ML have advanced at an impressive rate and have revealed the potential of chatbots in health care and clinical settings. AI technology outperforms humans in terms of image recognition, risk stratification, improved processing, and 24/7 assistance with data and analysis. However, there is no machine substitute for higher-level interactions, critical thinking, and ambiguity [<span class="footers"><a class="citation-link" href="#ref93" rel="footnote">93</a></span>]. Chatbots create added complexity that must be identified, addressed, and mitigated before their universal adoption in health care.</p><p class="abstract-paragraph">Hesitancy from physicians and poor adoption by patients is a major barrier to overcome, which could be explained by many of the factors discussed in this section. A cross-sectional web-based survey of 100 practicing physicians gathered the perceptions of chatbots in health care [<span class="footers"><a class="citation-link" href="#ref6" rel="footnote">6</a></span>]. Although a wide variety of beneficial aspects were reported (ie, management of health and administration), an equal number of concerns were present. Over 70% of physicians believe that chatbots cannot effectively care for all the patients’ needs, cannot display human emotion, cannot provide detailed treatment plans, and pose a risk if patients self-diagnose or do not fully comprehend their diagnosis. If the limitations of chatbots are better understood and mitigated, the fears of adopting this technology in health care may slowly subside. The <i>Discussion</i> section ends by exploring the challenges and questions for health care professionals, patients, and policy makers.</p><h4>Moral and Ethical Constraints</h4><p class="abstract-paragraph">The use of chatbots in health care presents a novel set of moral and ethical challenges that must be addressed for the public to fully embrace this technology. Issues to consider are privacy or confidentiality, informed consent, and fairness. Each of these concerns is addressed below. Although efforts have been made to address these concerns, current guidelines and policies are still far behind the rapid technological advances [<span class="footers"><a class="citation-link" href="#ref94" rel="footnote">94</a></span>].</p><p class="abstract-paragraph">Health care data are highly sensitive because of the risk of stigmatization and discrimination if the information is wrongfully disclosed. The ability of chatbots to ensure privacy is especially important, as vast amounts of personal and medical information are often collected without users being aware, including voice recognition and geographical tracking. The public’s lack of confidence is not surprising, given the increased frequency and magnitude of high-profile security breaches and inappropriate use of data [<span class="footers"><a class="citation-link" href="#ref95" rel="footnote">95</a></span>]. Unlike financial data that becomes obsolete after being stolen, medical data are particularly valuable, as they are not perishable. Privacy threats may break the trust that is essential to the therapeutic physician–patient relationship and inhibit open communication of relevant clinical information for proper diagnosis and treatment [<span class="footers"><a class="citation-link" href="#ref96" rel="footnote">96</a></span>].</p><p class="abstract-paragraph">Chatbots experience the <i>Black</i><i>Box</i> problem, which is similar to many computing systems programmed using ML that are trained on massive data sets to produce multiple layers of connections. Although they are capable of solving complex problems that are unimaginable by humans, these systems remain highly opaque, and the resulting solutions may be unintuitive. This means that the systems’ behavior is hard to explain by merely looking inside, and understanding exactly how they are programmed is nearly impossible. For both users and developers, transparency becomes an issue, as they are not able to fully understand the solution or intervene to predictably change the chatbot’s behavior [<span class="footers"><a class="citation-link" href="#ref97" rel="footnote">97</a></span>]. With the novelty and complexity of chatbots, obtaining valid informed consent where patients can make their own health-related risk and benefit assessments becomes problematic [<span class="footers"><a class="citation-link" href="#ref98" rel="footnote">98</a></span>]. Without sufficient transparency, deciding how certain decisions are made or how errors may occur reduces the reliability of the diagnostic process. The <i>Black Box</i> problem also poses a concern to patient autonomy by potentially undermining the shared decision-making between physicians and patients [<span class="footers"><a class="citation-link" href="#ref99" rel="footnote">99</a></span>]. The chatbot’s personalized suggestions are based on algorithms and refined based on the user’s past responses. The removal of options may slowly reduce the patient’s awareness of alternatives and interfere with free choice [<span class="footers"><a class="citation-link" href="#ref100" rel="footnote">100</a></span>].</p><p class="abstract-paragraph">Finally, the issue of fairness arises with algorithm bias when data used to train and test chatbots do not accurately reflect the people they represent [<span class="footers"><a class="citation-link" href="#ref101" rel="footnote">101</a></span>]. As the AI field lacks diversity, bias at the level of the algorithm and modeling choices may be overlooked by developers [<span class="footers"><a class="citation-link" href="#ref102" rel="footnote">102</a></span>]. In a study using 2 cases, differences in prediction accuracy were shown concerning gender and insurance type for intensive care unit mortality and psychiatric readmissions [<span class="footers"><a class="citation-link" href="#ref103" rel="footnote">103</a></span>]. On a larger scale, this may exacerbate barriers to health care for minorities or underprivileged individuals, leading to worse health outcomes. Identifying the source of algorithm bias is crucial for addressing health care disparities between various demographic groups and improving data collection.</p><h4>Chances for Errors</h4><p class="abstract-paragraph">Although studies have shown that AI technologies make fewer mistakes than humans in terms of diagnosis and decision-making, they still bear inherent risks for medical errors [<span class="footers"><a class="citation-link" href="#ref104" rel="footnote">104</a></span>]. The interpretation of speech remains prone to errors because of the complexity of background information, accuracy of linguistic unit segmentation, variability in acoustic channels, and linguistic ambiguity with homophones or semantic expressions. Chatbots are unable to efficiently cope with these errors because of the lack of common sense and the inability to properly model real-world knowledge [<span class="footers"><a class="citation-link" href="#ref105" rel="footnote">105</a></span>]. Another factor that contributes to errors and inaccurate predictions is the large, noisy data sets used to train modern models because large quantities of high-quality, representative data are often unavailable [<span class="footers"><a class="citation-link" href="#ref58" rel="footnote">58</a></span>]. In addition to the concern of accuracy and validity, addressing clinical utility and effectiveness of improving patients’ quality of life is just as important. With the increased use of diagnostic chatbots, the risk of overconfidence and overtreatment may cause more harm than benefit [<span class="footers"><a class="citation-link" href="#ref99" rel="footnote">99</a></span>]. There is still clear potential for improved decision-making, as diagnostic deep learning algorithms were found to be equivalent to health care professionals in classifying diseases in terms of accuracy [<span class="footers"><a class="citation-link" href="#ref106" rel="footnote">106</a></span>]. These issues presented above all raise the question of who is legally liable for medical errors. Avoiding responsibility becomes easier when numerous individuals are involved at multiple stages, from development to clinical applications [<span class="footers"><a class="citation-link" href="#ref107" rel="footnote">107</a></span>]. Although the law has been lagging and litigation is still a gray area, determining legal liability becomes increasingly pressing as chatbots become more accessible in health care.</p><h4>Regulatory Considerations</h4><p class="abstract-paragraph">Regulatory standards have been developed to accommodate for rapid modifications and ensure the safety and effectiveness of AI technology, including chatbots. The US Food and Drug Administration has recognized the distinctiveness of chatbots compared with traditional medical devices by defining the software within the medical device category and has outlined its approach through the Digital Health Innovation Action Plan [<span class="footers"><a class="citation-link" href="#ref108" rel="footnote">108</a></span>]. With the growing number of AI algorithms approved by the Food and Drug Administration, they opened public consultations for setting performance targets, monitoring performance, and reviewing when performance strays from preset parameters [<span class="footers"><a class="citation-link" href="#ref102" rel="footnote">102</a></span>]. The American Medical Association has also adopted the Augmented Intelligence in Health Care policy for the appropriate integration of AI into health care by emphasizing the design approach and enhancement of human intelligence [<span class="footers"><a class="citation-link" href="#ref109" rel="footnote">109</a></span>]. An area of concern is that chatbots are not covered under the Health Insurance Portability and Accountability Act; therefore, users’ data may be unknowingly sold, traded, and marketed by companies [<span class="footers"><a class="citation-link" href="#ref110" rel="footnote">110</a></span>]. On the other hand, overregulation may diminish the value of chatbots and decrease the freedom for innovators. Consequently, balancing these opposing aspects is essential to promote benefits and reduce harm to the health care system and society.</p><h4>Future Directions</h4><p class="abstract-paragraph">Chatbots’ robustness of integrating and learning from large clinical data sets, along with its ability to seamlessly communicate with users, contributes to its widespread integration in various health care components. Given the current status and challenges of cancer care, chatbots will likely be a key player in this field’s continual improvement. More specifically, they hold promise in addressing the triple aim of health care by improving the quality of care, bettering the health of populations, and reducing the burden or cost of our health care system. Beyond cancer care, there is an increasing number of creative ways in which chatbots could be applicable to health care. During the COVID-19 pandemic, chatbots were already deployed to share information, suggest behavior, and offer emotional support. They have the potential to prevent misinformation, detect symptoms, and lessen the mental health burden during global pandemics [<span class="footers"><a class="citation-link" href="#ref111" rel="footnote">111</a></span>]. At the global health level, chatbots have emerged as a socially responsible technology to provide equal access to quality health care and break down the barriers between the rich and poor [<span class="footers"><a class="citation-link" href="#ref112" rel="footnote">112</a></span>]. To further advance medicine and knowledge, the use of chatbots in education for learning and assessments is crucial for providing objective feedback, personalized content, and cost-effective evaluations [<span class="footers"><a class="citation-link" href="#ref113" rel="footnote">113</a></span>]. For example, the development of the Einstein app as a web-based physics teacher enables interactive learning and evaluations but is still far from being perfect [<span class="footers"><a class="citation-link" href="#ref114" rel="footnote">114</a></span>]. Given chatbots’ diverse applications in numerous aspects of health care, further research and interdisciplinary collaboration to advance this technology could revolutionize the practice of medicine.</p><p class="abstract-paragraph">On the basis of the discussion above, the following features are general directions of future suggestions for improvements in chatbots within cancer care in no particular order of importance:</p><ol type="1"><li class="spacey">Patients with cancer may feel vulnerable or fear discrimination from employers or society [<span class="footers"><a class="citation-link" href="#ref115" rel="footnote">115</a></span>]. Security of sensitive information must be held to the highest standards, especially when personal health information is shared between providers and hospital systems.</li><li class="spacey">An increasing number of patients are bringing internet-based information to consultations that are not critically assessed for trustworthiness or credibility. If used correctly, the additional health information could enhance understanding, improve the ability to manage their conditions, and increase confidence during interaction with physicians [<span class="footers"><a class="citation-link" href="#ref116" rel="footnote">116</a></span>]. Unfortunately, this is often not the case, and most patients are not adequately informed regarding the proper screening of information. Ways to address this challenge include promoting awareness and developing patient management guidelines. Chatbots also have the potential to become a key player in their ability to screen for credible information. They could help vulnerable individuals critically navigate web-based cancer information, especially for the older or more chronic populations that tend to be less technologically adept.</li><li class="spacey">Current applications of chatbots as computerized decision support systems for diagnosis and treatment are relatively limited. The targeted audience for most has been for patients’ use, and few are designed to aid physicians at the point of care. Medical Sieve and Watson for Oncology are the only chatbots found in our search that are designed specifically for clinicians. There are far more AI tools in the market to help with clinical decision-making without the ability to interact with users [<span class="footers"><a class="citation-link" href="#ref117" rel="footnote">117</a></span>]. With the rapid data collection from electronic health records, real-time predictions, and links to clinical recommendations, adding chatbot functionalities to current decision aids will only improve patient-centered care and streamline the workflow for clinicians.</li><li class="spacey">More concrete evidence of high quality and accuracy across a broad range of conditions and populations entails more representative training data reflecting racial biases and developing peer-reviewed algorithms to reduce the <i>Black Box</i> problem.</li><li class="spacey">Integration into the health care system, particularly with telemedicine, for seamless delivery from the beginning to the end does not mean replacing in-person care but rather complementing the health care workflow to ensure patients receive continuity and coordination of care.</li><li class="spacey">Reimbursement of chatbot services to physicians who decide to implement this technology into their practice will likely increase adoption rates. Organizations and health providers will likely profit because chatbots allow for a more efficient and reduced cost of delivery.</li><li class="spacey">Continual training of chatbots as new knowledge is uncovered, such as symptom patterns or standard of care, is needed.</li><li class="spacey">As the Vik study found that users were more likely to respond to multiple-choice questions over open-ended ones [<span class="footers"><a class="citation-link" href="#ref38" rel="footnote">38</a></span>], chatbot developers should move toward the choice with higher response rates. Studies, surveys, and focus groups should continue to be conducted to determine the best ways to converse with users.</li><li class="spacey">Universal adoption of various technical features, such as training with additional languages, image recognition, voice recognition, user feedback to improve services according to needs, access on multiple common platforms, and reacting to unexpected responses, need to be considered.</li></ol><p class="abstract-paragraph">The ability to accurately measure performance is critical for continuous feedback and improvement of chatbots, especially the high standards and vulnerable individuals served in health care. Given that the introduction of chatbots to cancer care is relatively recent, rigorous evidence-based research is lacking. Standardized indicators of success between users and chatbots need to be implemented by regulatory agencies before adoption. Once the primary purpose is defined, common quality indicators to consider are the success rate of a given action, nonresponse rate, comprehension quality, response accuracy, retention or adoption rates, engagement, and satisfaction level. The ultimate goal is to assess whether chatbots positively affect and address the 3 aims of health care. Regular quality checks are especially critical for chatbots acting as decision aids because they can have a major impact on patients’ health outcomes.</p><h4>Review Limitations</h4><p class="abstract-paragraph">The systematic literature review and chatbot database search includes a few limitations. The literature review and chatbot search were all conducted by a single reviewer, which could have potentially introduced bias and limited findings. In addition, our review explored a broad range of health care topics, and some areas could have been elaborated upon and explored more deeply. Furthermore, only a limited number of studies were included for each subtopic of chatbots for oncology apps because of the scarcity of studies addressing this topic. Future studies should consider refining the search strategy to identify other potentially relevant sources that may have been overlooked and assign multiple reviews to limit individual bias.</p><h4>Conclusions</h4><p class="abstract-paragraph">As illustrated in this review, these chatbots’ potential in cancer diagnostics and treatment, patient monitoring and support, clinical workflow efficiency, and health promotion have yet to be fully explored. Numerous risks and challenges will continue to arise that require careful navigation with the rapid advancements in chatbots. Consequently, weighing the gains versus threats with a critical eye is imperative. Even after laying down the proper foundations for using chatbots safely and effectively, the human element in the practice of medicine is irreplaceable and will always be present. Health care professionals have the responsibility of understanding both the benefits and risks associated with chatbots and, in turn, educating their patients.</p></article><p><h4 class="h4-border-top">Acknowledgments</h4></p><p class="abstract-paragraph">This work was supported by a Canadian Institutes of Health Research Planning and Dissemination Grant—Institute Community Support under grant number CIHR PCS-168296.</p><h4 class="h4-border-top">Conflicts of Interest</h4><p><p class="abstract-paragraph">None declared.</p></p><div class="footnotes"><h4 id="References" class="h4-border-top navigation-heading" data-label="References">References</h4><ol><li><span id="ref1">Kersting K. Machine learning and artificial intelligence: two fellow travelers on the quest for intelligent behavior in machines. 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[<a target="_blank" href="https://dx.doi.org/10.1016/j.amepre.2011.01.014">CrossRef</a>]</span></li></ol></div><br><hr><a name="Abbreviations">&lrm;</a><h4 class="navigation-heading" id="Abbreviations" data-label="Abbreviations">Abbreviations</h4><table width="80%" border="0" align="center"><tr><td><b>AI:</b> artificial intelligence</td></tr><tr><td><b>ML:</b> machine learning</td></tr></table><br><hr><p style="font-style: italic">Edited by G Eysenbach; submitted 09.02.21; peer-reviewed by M Falahee, H Turbe, S Ng; comments to author 10.06.21; revised version received 02.07.21; accepted 18.09.21; published 29.11.21</p><a href="https://support.jmir.org/hc/en-us/articles/115002955531" id="Copyright" target="_blank" class="navigation-heading h4 d-block" aria-label="Copyright - what is a Creative Commons License?" data-label="Copyright">Copyright <span class="fas fa-question-circle"></span></a><p class="article-copyright">©Lu Xu, Leslie Sanders, Kay Li, James C L Chow. Originally published in JMIR Cancer (https://cancer.jmir.org), 29.11.2021.</p><small class="article-license"><p class="abstract-paragraph">This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on https://cancer.jmir.org/, as well as this copyright and license information must be included.</p></small><br></section></article></section></section></main> </div></div></div></div> <aside data-test="sidebar-exists" class="sidebar-citation col-lg-3 mb-5"> <div><h2 class="h4 green-heading-underline width-fit-content"> Citation </h2> <p class="fw-bold"> Please cite as: </p> <p><span> Xu L<span>,</span></span><span> Sanders L<span>,</span></span><span> Li K<span>,</span></span><span> Chow JCL</span> <br> <span>Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review</span> <br> <span>JMIR Cancer 2021;7(4):e27850</span> <br> <span>doi: <span><a aria-label="DOI number 10.2196/27850" data-test="article-doi" target="_blank" href="https://doi.org/10.2196/27850"> 10.2196/27850 </a></span></span> <span style="display: block"> PMID: <span><a 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class=\"authors-container\"\u003E\u003Cdiv class=\"authors clearfix\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"authors-container\"\u003E\u003Cdiv class=\"authors clearfix\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"authors-container\"\u003E\u003Cdiv class=\"authors clearfix\"\u003E\u003Cul class=\"clearfix\"\u003E\u003Cli\u003E\u003Ca href=\"\u002Fsearch\u002FsearchResult?field%5B%5D=author&criteria%5B%5D=Lu+Xu\" class=\"btn-view-author-options\"\u003ELu Xu\u003Csup\u003E\u003Csmall\u003E1,\u003C\u002Fsmall\u003E\u003C\u002Fsup\u003E\u003Csup\u003E\u003Csmall\u003E2\u003C\u002Fsmall\u003E\u003C\u002Fsup\u003E, MEng\u003C\u002Fa\u003E\u003Ca class=\"author-orcid\" href=\"https:\u002F\u002Forcid.org\u002F0000-0002-8485-9968\" target=\"_blank\" title=\"ORCID\"\u003E \u003C\u002Fa\u003E; \u003C\u002Fli\u003E\u003Cli\u003E\u003Ca href=\"\u002Fsearch\u002FsearchResult?field%5B%5D=author&criteria%5B%5D=Leslie+Sanders\" class=\"btn-view-author-options\"\u003ELeslie Sanders\u003Csup\u003E\u003Csmall\u003E3\u003C\u002Fsmall\u003E\u003C\u002Fsup\u003E, PhD\u003C\u002Fa\u003E\u003Ca class=\"author-orcid\" href=\"https:\u002F\u002Forcid.org\u002F0000-0002-6990-7722\" target=\"_blank\" title=\"ORCID\"\u003E \u003C\u002Fa\u003E; \u003C\u002Fli\u003E\u003Cli\u003E\u003Ca href=\"\u002Fsearch\u002FsearchResult?field%5B%5D=author&criteria%5B%5D=Kay+Li\" class=\"btn-view-author-options\"\u003EKay Li\u003Csup\u003E\u003Csmall\u003E4\u003C\u002Fsmall\u003E\u003C\u002Fsup\u003E, PhD\u003C\u002Fa\u003E\u003Ca class=\"author-orcid\" href=\"https:\u002F\u002Forcid.org\u002F0000-0002-5765-1635\" target=\"_blank\" title=\"ORCID\"\u003E \u003C\u002Fa\u003E; \u003C\u002Fli\u003E\u003Cli\u003E\u003Ca href=\"\u002Fsearch\u002FsearchResult?field%5B%5D=author&criteria%5B%5D=James%20C%20L+Chow\" class=\"btn-view-author-options\"\u003EJames C L Chow\u003Csup\u003E\u003Csmall\u003E5,\u003C\u002Fsmall\u003E\u003C\u002Fsup\u003E\u003Csup\u003E\u003Csmall\u003E6\u003C\u002Fsmall\u003E\u003C\u002Fsup\u003E, PhD\u003C\u002Fa\u003E\u003Ca class=\"author-orcid\" href=\"https:\u002F\u002Forcid.org\u002F0000-0003-4202-4855\" target=\"_blank\" title=\"ORCID\"\u003E \u003C\u002Fa\u003E\u003C\u002Fli\u003E\u003C\u002Ful\u003E\u003Cdiv class=\"author-affiliation-details\"\u003E\u003Cp\u003E\u003Csup\u003E1\u003C\u002Fsup\u003EInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada\u003C\u002Fp\u003E\u003Cp\u003E\u003Csup\u003E2\u003C\u002Fsup\u003EDepartment of Medical Biophysics, Western University, London, ON, Canada\u003C\u002Fp\u003E\u003Cp\u003E\u003Csup\u003E3\u003C\u002Fsup\u003EDepartment of Humanities, York University, Toronto, ON, Canada\u003C\u002Fp\u003E\u003Cp\u003E\u003Csup\u003E4\u003C\u002Fsup\u003EDepartment of English, York University, Toronto, ON, Canada\u003C\u002Fp\u003E\u003Cp\u003E\u003Csup\u003E5\u003C\u002Fsup\u003EDepartment of Medical Physics, Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada\u003C\u002Fp\u003E\u003Cp\u003E\u003Csup\u003E6\u003C\u002Fsup\u003EDepartment of Radiation Oncology, University of Toronto, Toronto, ON, Canada\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"corresponding-author-and-affiliations clearfix\"\u003E\u003Cdiv class=\"corresponding-author-details\"\u003E\u003Ch3\u003ECorresponding Author:\u003C\u002Fh3\u003E\u003Cp\u003EJames C L Chow, PhD\u003C\u002Fp\u003E\u003Cp\u003E\u003C\u002Fp\u003E\u003Cp\u003EDepartment of Medical Physics, Radiation Medicine Program\u003C\u002Fp\u003E\u003Cp\u003EPrincess Margaret Cancer Centre\u003C\u002Fp\u003E\u003Cp\u003EUniversity Health Network\u003C\u002Fp\u003E\u003Cp\u003E7\u002FF, 700 University Avenue\u003C\u002Fp\u003E\u003Cp\u003EToronto, ON, M5G 1X6\u003C\u002Fp\u003E\u003Cp\u003ECanada\u003C\u002Fp\u003E\u003Cp\u003EPhone: 1 9464501 ext 5089\u003C\u002Fp\u003E\u003Cp\u003EFax:1 9466566\u003C\u002Fp\u003E\u003Cp\u003EEmail: \u003Ca href=\"mailto:james.chow@rmp.uhn.ca\"\u003Ejames.chow@rmp.uhn.ca\u003C\u002Fa\u003E\u003C\u002Fp\u003E\u003Cbr\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Csection class=\"article-content clearfix\"\u003E\u003Carticle class=\"abstract\"\u003E\u003Ch3 id=\"Abstract\" class=\"navigation-heading\" data-label=\"Abstract\"\u003EAbstract\u003C\u002Fh3\u003E\u003Cp\u003E\u003Cspan class=\"abstract-sub-heading\"\u003EBackground: \u003C\u002Fspan\u003EChatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility.\u003Cbr\u003E\u003C\u002Fp\u003E\u003Cp\u003E\u003Cspan class=\"abstract-sub-heading\"\u003EObjective: \u003C\u002Fspan\u003EThis review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation.\u003Cbr\u003E\u003C\u002Fp\u003E\u003Cp\u003E\u003Cspan class=\"abstract-sub-heading\"\u003EMethods: \u003C\u002Fspan\u003EA search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion.\u003Cbr\u003E\u003C\u002Fp\u003E\u003Cp\u003E\u003Cspan class=\"abstract-sub-heading\"\u003EResults: \u003C\u002Fspan\u003EEven after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored.\u003Cbr\u003E\u003C\u002Fp\u003E\u003Cp\u003E\u003Cspan class=\"abstract-sub-heading\"\u003EConclusions: \u003C\u002Fspan\u003EFurther research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.\u003Cbr\u003E\u003C\u002Fp\u003E\u003Cstrong class=\"h4-article-volume-issue\"\u003EJMIR Cancer 2021;7(4):e27850\u003C\u002Fstrong\u003E\u003Cbr\u003E\u003Cbr\u003E\u003Cspan class=\"article-doi\"\u003E\u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.2196\u002F27850\"\u003Edoi:10.2196\u002F27850\u003C\u002Fa\u003E\u003C\u002Fspan\u003E\u003Cbr\u003E\u003Cbr\u003E\u003Ch3 class=\"h3-main-heading\" id=\"Keywords\"\u003EKeywords\u003C\u002Fh3\u003E\u003Cdiv class=\"keywords\"\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=chatbot&precise=true\"\u003Echatbot\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=artificial%20intelligence&precise=true\"\u003Eartificial intelligence\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=machine%20learning&precise=true\"\u003Emachine learning\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=health&precise=true\"\u003Ehealth\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=medicine&precise=true\"\u003Emedicine\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=communication&precise=true\"\u003Ecommunication\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=diagnosis&precise=true\"\u003Ediagnosis\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=cancer%20therapy&precise=true\"\u003Ecancer therapy\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=ethics&precise=true\"\u003Eethics\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=medical%20biophysics&precise=true\"\u003Emedical biophysics\u003C\u002Fa\u003E; \u003C\u002Fspan\u003E\u003Cspan\u003E\u003Ca href=\"\u002Fsearch?type=keyword&term=mobile%20phone&precise=true\"\u003Emobile phone\u003C\u002Fa\u003E \u003C\u002Fspan\u003E\u003C\u002Fdiv\u003E\u003Cdiv id=\"trendmd-suggestions\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Farticle\u003E\u003Cbr\u003E\u003Carticle class=\"main-article clearfix\"\u003E\u003Cbr\u003E\u003Ch3 class=\"navigation-heading h3-main-heading\" id=\"Introduction\" data-label=\"Introduction\"\u003EIntroduction\u003C\u002Fh3\u003E\u003Ch4\u003EBackground\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EArtificial intelligence (AI) is at the forefront of transforming numerous aspects of our lives by modifying the way we analyze information and improving decision-making through problem solving, reasoning, and learning. Machine learning (ML) is a subset of AI that improves its performance based on the data provided to a generic algorithm from experience rather than defining rules in traditional approaches [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref1\" rel=\"footnote\"\u003E1\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Advancements in ML have provided benefits in terms of accuracy, decision-making, quick processing, cost-effectiveness, and handling of complex data [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref2\" rel=\"footnote\"\u003E2\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Chatbots, also known as chatter robots, smart bots, conversational agents, digital assistants, or intellectual agents, are prime examples of AI systems that have evolved from ML. The Oxford dictionary defines a chatbot as “a computer program that can hold a conversation with a person, usually over the internet\u003Ci\u003E.\u003C\u002Fi\u003E” They can also be physical entities designed to socially interact with humans or other robots. Predetermined responses are then generated by analyzing user input, on text or spoken ground, and accessing relevant knowledge [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref3\" rel=\"footnote\"\u003E3\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Problems arise when dealing with more complex situations in dynamic environments and managing social conversational practices according to specific contexts and unique communication strategies [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref4\" rel=\"footnote\"\u003E4\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EGiven these effectual benefits, it is not surprising that chatbots have rapidly evolved over the past 2 decades and integrated themselves into numerous fields, such as entertainment, travel, gaming, robotics, and security. Chatbots have been proven to be particularly applicable in various health care components that usually involve face-to-face interactions. With their ability for complex dialog management and conversational flexibility, integration of chatbot technology into clinical practice may reduce costs, refine workflow efficiencies, and improve patient outcomes [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref5\" rel=\"footnote\"\u003E5\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. A web-based, self-report survey examining physicians’ perspectives found positive benefits of health care chatbots in managing one’s own health; for improved physical, psychological, and behavioral outcomes; and most notably, for administrative purposes [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref6\" rel=\"footnote\"\u003E6\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. In light of the opportunities provided by this relatively new technology, potential limitations and areas of concern may arise that could potentially harm users. Concerns regarding accuracy, cybersecurity, lack of empathy, and technological maturity are reported as potential factors associated with the delay in chatbot acceptability or integration into health care [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref7\" rel=\"footnote\"\u003E7\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Ch4\u003EObjectives\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EThis narrative review paper reports on health care components for chatbots, with a focus on cancer therapy. The rest of this paper is organized as follows: first, we introduce the developmental progress with a general overview of the architecture, design concepts, and types of chatbots; the main \u003Ci\u003EResults\u003C\u002Fi\u003E section focuses on the role that chatbots play in areas related to oncology, such as diagnosis, treatment, monitoring, support, workflow efficiency, and health promotion; and the \u003Ci\u003EDiscussion\u003C\u002Fi\u003E section analyzes potential limitations and concerns for successful implementation while addressing future applications and research topics.\u003C\u002Fp\u003E\u003Cbr\u003E\u003Ch3 class=\"navigation-heading h3-main-heading\" id=\"Methods\" data-label=\"Methods\"\u003EMethods\u003C\u002Fh3\u003E\u003Cp class=\"abstract-paragraph\"\u003EThis review focuses on articles from peer-reviewed journals and conference proceedings. The following databases were searched from October to December 2020 for relevant and current studies from 2000 to 2020: IEEE Xplore, PubMed, Web of Science, Scopus, and OVID. The literature search used the following key terms: \u003Ci\u003Echatbot\u003C\u002Fi\u003E, \u003Ci\u003Echatter robot\u003C\u002Fi\u003E, \u003Ci\u003Econversational agent\u003C\u002Fi\u003E, \u003Ci\u003Eartificial intelligence\u003C\u002Fi\u003E, and \u003Ci\u003Emachine learning\u003C\u002Fi\u003E. For further refinement, these key terms were combined with more specific terms aligned with the focus of the paper. This included \u003Ci\u003Ehealthcare\u003C\u002Fi\u003E, \u003Ci\u003Ecancer therapy\u003C\u002Fi\u003E, \u003Ci\u003Eoncology\u003C\u002Fi\u003E, \u003Ci\u003Ediagnosis\u003C\u002Fi\u003E, \u003Ci\u003Etreatment\u003C\u002Fi\u003E, \u003Ci\u003Eradiation therapy\u003C\u002Fi\u003E, and \u003Ci\u003Eradiotherapy\u003C\u002Fi\u003E. The searches were not limited by language or study design. Letters and technical reports were excluded from the search. The full list of sources and search strategies is available from the authors.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EThe screening of chatbots was guided by a systematic review process from the Botlist directory during the period of January 2021. This directory was chosen as it was open-access and categorized the chatbots under many different categories (ie, health care, communication, and entertainment) and contained many commonly used messaging services (ie, Facebook Messenger, Discord, Slack, Kik, and Skype). A total of 78 chatbots were identified for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. It should be noted that using the health filters from a web directory limits the results to the search strategy and marketing label. Thus, the results from equivalent studies may differ when repeated.\u003C\u002Fp\u003E\u003Cbr\u003E\u003Ch3 class=\"navigation-heading h3-main-heading\" id=\"Results\" data-label=\"Results\"\u003EResults\u003C\u002Fh3\u003E\u003Ch4\u003EChatbot History and Evolution\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EThe idea of a chatbot was first introduced in 1950 when Alan Turing proposed the question, “Can machines think?” [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref8\" rel=\"footnote\"\u003E8\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The earliest forms were designed to pass the Turing test and mimic human conversations as much as possible. In 1966, ELIZA (MIT Artificial Intelligence Library) was the first known chatbot developed to act as a psychotherapist, using pattern matching and template-based responses to converse in a question-based format [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref9\" rel=\"footnote\"\u003E9\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Improvements were made to build a more human-like and personalized entity by incorporating a personality in PARRY (developed Kenneth Colby) that simulated a paranoid patient [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref10\" rel=\"footnote\"\u003E10\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. One of the most well-known chatbots is ALICE, developed in 1995 by Richard Wallace, which uses a pattern-matching technique to retrieve example sentences from output templates and avoid inappropriate responses [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref11\" rel=\"footnote\"\u003E11\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. A renewed interest in AI and advances in ML have led to the growing use and availability of chatbots in various fields [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref12\" rel=\"footnote\"\u003E12\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. SmarterChild (ActiveBuddy, Inc) [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref13\" rel=\"footnote\"\u003E13\u003C\u002Fa\u003E\u003C\u002Fspan\u003E] became widely accessible through messenger apps, followed by more familiar web-based assistants using voice-activated systems, such as Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana. On the basis of our analysis (\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#figure1\" rel=\"footnote\"\u003EFigure 1\u003C\u002Fa\u003E\u003C\u002Fspan\u003E), the most popular developments of chatbots for health care purposes are diagnostics, patient support (ie, mental health counseling), and health promotion. Some of these applications will be further explored in the following section for cancer applications.\u003C\u002Fp\u003E\u003Cfigure\u003E\u003Ca name=\"figure1\"\u003E&lrm;\u003C\u002Fa\u003E\u003Ca class=\"fancybox\" title=\"Figure 1. Search and screening for health care chatbots. Chatbots using more than one platform are included.\" href=\"https:\u002F\u002Fasset.jmir.pub\u002Fassets\u002F0ecd31b048dfddf81d5d59d10f97c57a.png\" id=\"figure1\"\u003E\u003Cimg class=\"figure-image\" src=\"https:\u002F\u002Fasset.jmir.pub\u002Fassets\u002F0ecd31b048dfddf81d5d59d10f97c57a.png\"\u003E\u003C\u002Fa\u003E\u003Cfigcaption\u003E\u003Cspan class=\"typcn typcn-image\"\u003E\u003C\u002Fspan\u003EFigure 1. Search and screening for health care chatbots. Chatbots using more than one platform are included. \u003C\u002Ffigcaption\u003E\u003Ca class=\"fancybox\" href=\"https:\u002F\u002Fasset.jmir.pub\u002Fassets\u002F0ecd31b048dfddf81d5d59d10f97c57a.png\" title=\"Figure 1. Search and screening for health care chatbots. Chatbots using more than one platform are included.\"\u003EView this figure\u003C\u002Fa\u003E\u003C\u002Ffigure\u003E\u003Ch4\u003EChatbot General Architecture\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EAlthough there are a variety of techniques for the development of chatbots, the general layout is relatively straightforward. As a computer application that uses ML to mimic human conversation, the underlying concept is similar for all types with 4 essential stages (input processing, input understanding, response generation, and response selection) [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref14\" rel=\"footnote\"\u003E14\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. A simplified general chatbot architecture is illustrated in \u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#figure2\" rel=\"footnote\"\u003EFigure 2\u003C\u002Fa\u003E\u003C\u002Fspan\u003E. First, the user makes a request, in text or speech format, which is received and interpreted by the chatbot. From there, the processed information could be remembered, or more details could be requested for clarification. After the request is understood, the requested actions are performed, and the data of interest are retrieved from the database or external sources [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref15\" rel=\"footnote\"\u003E15\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cfigure\u003E\u003Ca name=\"figure2\"\u003E&lrm;\u003C\u002Fa\u003E\u003Ca class=\"fancybox\" title=\"Figure 2. Schematic representation of general chatbot architecture.\" href=\"https:\u002F\u002Fasset.jmir.pub\u002Fassets\u002Fc59c57b737af6a17b92c8286720da480.png\" id=\"figure2\"\u003E\u003Cimg class=\"figure-image\" src=\"https:\u002F\u002Fasset.jmir.pub\u002Fassets\u002Fc59c57b737af6a17b92c8286720da480.png\"\u003E\u003C\u002Fa\u003E\u003Cfigcaption\u003E\u003Cspan class=\"typcn typcn-image\"\u003E\u003C\u002Fspan\u003EFigure 2. Schematic representation of general chatbot architecture. \u003C\u002Ffigcaption\u003E\u003Ca class=\"fancybox\" href=\"https:\u002F\u002Fasset.jmir.pub\u002Fassets\u002Fc59c57b737af6a17b92c8286720da480.png\" title=\"Figure 2. Schematic representation of general chatbot architecture.\"\u003EView this figure\u003C\u002Fa\u003E\u003C\u002Ffigure\u003E\u003Ch4\u003EChatbot Types\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EWith the vast number of algorithms, tools, and platforms available, understanding the different types and end purposes of these chatbots will assist developers in choosing the optimal tools when designing them to fit the specific needs of users. These categories are not exclusive, as chatbots may possess multiple characteristics, making the process more variable. The 5 main types are described below [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref15\" rel=\"footnote\"\u003E15\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. \u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#box1\" rel=\"footnote\"\u003ETextbox 1\u003C\u002Fa\u003E\u003C\u002Fspan\u003E describes some examples of the recommended apps for each type of chatbot but are not limited to the ones specified.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EKnowledge domain classification is based on accessible knowledge or the data used to train the chatbot. Under this category are the open domain for general topics and the closed domain focusing on more specific information. Service-provided classification is dependent on sentimental proximity to the user and the amount of intimate interaction dependent on the task performed. This can be further divided into interpersonal for providing services to transmit information, intrapersonal for companionship or personal support to humans, and interagent to communicate with other chatbots [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref14\" rel=\"footnote\"\u003E14\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The next classification is based on goals with the aim of achievement, subdivided into informative, conversational, and task based. Response generation chatbots, further classified as rule based, retrieval based, and generative, account for the process of analyzing inputs and generating responses [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref16\" rel=\"footnote\"\u003E16\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Finally, human-aided classification incorporates human computation, which provides more flexibility and robustness but lacks the speed to accommodate more requests [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref17\" rel=\"footnote\"\u003E17\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cdiv class=\"textbox-container\" id=\"box1\"\u003E\u003Ch5\u003ERecommended health care components for the different types of chatbots.\u003C\u002Fh5\u003E\u003Cp class=\"abstract-paragraph\"\u003E\u003Cb\u003EKnowledge domain\u003C\u002Fb\u003E\u003C\u002Fp\u003E\u003Cul\u003E\u003Cli class=\"spacey\"\u003EOpen domain: responding to more general and broader topics that can be easily searched within databases; may be the preferred chatbot type for routine symptom screening, connecting to providers or services, or health promotion apps\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EClosed domain: responding to complex or specific questions requiring more in-depth research; may be the preferred chatbot type for treatment planning or recommendation\u003C\u002Fli\u003E\u003C\u002Ful\u003E\u003Cp class=\"abstract-paragraph\"\u003E\u003Cb\u003EService provided\u003C\u002Fb\u003E\u003C\u002Fp\u003E\u003Cul\u003E\u003Cli class=\"spacey\"\u003EInterpersonal: used mainly to transmit information without much intimate connection with users; may be the preferred chatbot type for imaging diagnostics or hereditary assessment where the main duty is to relay factual information to users\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EIntrapersonal: tailored for companionship or support; may be the preferred chatbot type for counseling, emotional support, or health promotion that requires a sense of human touch\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EInteragent: used for communicating with other chatbots or computer systems; may be the preferred chatbot type for administration purposes when transferring patient information between locations\u003C\u002Fli\u003E\u003C\u002Ful\u003E\u003Cp class=\"abstract-paragraph\"\u003E\u003Cb\u003EGoal based\u003C\u002Fb\u003E\u003C\u002Fp\u003E\u003Cul\u003E\u003Cli class=\"spacey\"\u003EInformative: designed to provide information from warehouse database or inventory entry; may be the preferred chatbot type for connecting patients with resources or remote patient monitoring\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EConversational: built with the purpose of conversing with users as naturally as possible; may be the preferred chatbot type for counseling, emotional support, or health promotion\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003ETask based: only performs 1 specific task where actions are predetermined; may be the preferred chatbot type for screening and diagnostics\u003C\u002Fli\u003E\u003C\u002Ful\u003E\u003Cp class=\"abstract-paragraph\"\u003E\u003Cb\u003EResponse generation\u003C\u002Fb\u003E\u003C\u002Fp\u003E\u003Cul\u003E\u003Cli class=\"spacey\"\u003EUses pattern matching when the domain is narrow and sufficient data are available to train the system; may be the preferred chatbot type for screening and diagnostics\u003C\u002Fli\u003E\u003C\u002Ful\u003E\u003Cp class=\"abstract-paragraph\"\u003E\u003Cb\u003EHuman aided\u003C\u002Fb\u003E\u003C\u002Fp\u003E\u003Cul\u003E\u003Cli class=\"spacey\"\u003EIncorporates human computation that increases flexibility and robustness but decreases speed; may be the preferred chatbot type for most apps except for support or workflow efficiency, where speed is an essential factor in the delivery of care\u003C\u002Fli\u003E\u003C\u002Ful\u003E\u003Cfigcaption\u003E\u003Cspan class=\"typcn typcn-image\"\u003E\u003C\u002Fspan\u003ETextbox 1. Recommended health care components for the different types of chatbots.\u003C\u002Ffigcaption\u003E\u003C\u002Fdiv\u003E\u003Ch4\u003EChatbots in Cancer Therapy\u003C\u002Fh4\u003E\u003Ch5\u003EOverview\u003C\u002Fh5\u003E\u003Cp class=\"abstract-paragraph\"\u003ECancer has become a major health crisis and is the second leading cause of death in the United States [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref18\" rel=\"footnote\"\u003E18\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The exponentially increasing number of patients with cancer each year may be because of a combination of carcinogens in the environment and improved quality of care. The latter aspect could explain why cancer is slowly becoming a chronic disease that is manageable over time [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref19\" rel=\"footnote\"\u003E19\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Added life expectancy poses new challenges for both patients and the health care team. For example, many patients now require extended at-home support and monitoring, whereas health care workers deal with an increased workload. Although clinicians’ knowledge base in the use of scientific evidence to guide decision-making has expanded, there are still many other facets to the quality of care that has yet to catch up. Key areas of focus are safety, effectiveness, timeliness, efficiency, equitability, and patient-centered care [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref20\" rel=\"footnote\"\u003E20\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EChatbots have the potential to address many of the current concerns regarding cancer care mentioned above. This includes the triple aim of health care that encompasses improving the experience of care, improving the health of populations, and reducing per capita costs [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref21\" rel=\"footnote\"\u003E21\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Chatbots can improve the quality or experience of care by providing efficient, equitable, and personalized medical services. We can think of them as intermediaries between physicians for facilitating the history taking of sensitive and intimate information before consultations. They could also be thought of as decision aids that deliver regular feedback on disease progression and treatment reactions to help clinicians better understand individual conditions. Preventative measures of cancer have become a priority worldwide, as early detection and treatment alone have not been effective in eliminating this disease [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref22\" rel=\"footnote\"\u003E22\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Physical, psychological, and behavioral improvements of underserved or vulnerable populations may even be possible through chatbots, as they are so readily accessible through common messaging platforms. Health promotion use, such as lifestyle coaching, healthy eating, and smoking cessation, has been one of the most common chatbots according to our search. In addition, chatbots could help save a significant amount of health care costs and resources. Newer therapeutic innovations have come with a heavy price tag, and out-of-pocket expenses have placed a significant strain on patients’ financial well-being [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref23\" rel=\"footnote\"\u003E23\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. With chatbots implemented in cancer care, consultations for minor health concerns may be avoided, which allows clinicians to spend more time with patients who need their attention the most. Costs may also be reduced by delivering medical services more efficiently. For example, the workflow can be streamlined by assisting physicians in administrative tasks, such as scheduling appointments, providing medical information, or locating clinics.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EWith the rapidly increasing applications of chatbots in health care, this section will explore several areas of development and innovation in cancer care. Various examples of current chatbots provided below will illustrate their ability to tackle the triple aim of health care. The specific use case of chatbots in oncology with examples of actual products and proposed designs are outlined in \u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#table1\" rel=\"footnote\"\u003ETable 1\u003C\u002Fa\u003E\u003C\u002Fspan\u003E.\u003C\u002Fp\u003E\u003Cdiv class=\"figure-table\"\u003E\u003Cfigcaption\u003E\u003Cspan class=\"typcn typcn-clipboard\"\u003E\u003C\u002Fspan\u003ETable 1.\n Use case for chatbots in oncology, with examples of current specific applications or proposed designs.\u003C\u002Ffigcaption\u003E\u003Ctable width=\"1000\" cellpadding=\"5\" cellspacing=\"0\" border=\"1\" rules=\"groups\" frame=\"hsides\"\u003E\u003Ccol width=\"30\" span=\"1\"\u003E\u003Ccol width=\"30\" span=\"1\"\u003E\u003Ccol width=\"300\" span=\"1\"\u003E\u003Ccol width=\"0\" span=\"1\"\u003E\u003Ccol width=\"640\" span=\"1\"\u003E\u003Cthead\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003EUse case and application, chatbot\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EFunction\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003C\u002Fthead\u003E\u003Ctbody\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd colspan=\"5\" rowspan=\"1\"\u003E\u003Cb\u003EScreening and diagnosis\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003EImaging diagnostic\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EMedical Sieve [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref24\" rel=\"footnote\"\u003E24\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EExamines radiological images to aid clinicians with diagnosis\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003ESymptom screening\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EQuro [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref25\" rel=\"footnote\"\u003E25\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EPresynopsis based on symptoms and history to predict user conditions\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EBuoy Health [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref26\" rel=\"footnote\"\u003E26\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EAssists in identifying the cause of illnesses and provides medical advice\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EHarshitha breast cancer screening [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref27\" rel=\"footnote\"\u003E27\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EDialog flow to give an initial analysis of breast cancer symptoms\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EBabylon [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref28\" rel=\"footnote\"\u003E28\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ESymptom checker\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EYour.md [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref28\" rel=\"footnote\"\u003E28\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ESymptom checker\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EAda [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref28\" rel=\"footnote\"\u003E28\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ESymptom checker\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003EHereditary assessment\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EItRuns [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref29\" rel=\"footnote\"\u003E29\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EGathers family history information at the population level to determine the risk of hereditary cancer\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd colspan=\"5\" rowspan=\"1\"\u003E\u003Cb\u003ETreatment\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003EPatient treatment recommendation\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EMathew [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref30\" rel=\"footnote\"\u003E30\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EIdentifies symptoms, predicts the disease using a symptom–disease data set, and recommends a suitable treatment\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EMadhu [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref31\" rel=\"footnote\"\u003E31\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EProvides a list of available treatments for various diseases and informs the user of the composition and prescribed use of the medications\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003EConnecting patients with providers or resources\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EDivya [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref32\" rel=\"footnote\"\u003E32\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EEngages patients regarding their symptoms to provide a personalized diagnosis and connects with appropriate medical service\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003ERarhi [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref33\" rel=\"footnote\"\u003E33\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EProvides a diagnosis based on symptoms, measures the seriousness, and connects with a physician\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003E\u003Cb\u003EPhysician treatment planning\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EWatson for Oncology [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref34\" rel=\"footnote\"\u003E34\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EExamines data from records and medical notes to generate an evidence-based treatment plan for oncologists\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd colspan=\"5\" rowspan=\"1\"\u003E\u003Cb\u003EMonitoring\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003ERemote patient monitoring\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003ESTREAMD [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref35\" rel=\"footnote\"\u003E35\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EProvides access to care instructions and educational information\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EConversa [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref35\" rel=\"footnote\"\u003E35\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EProvides access to care instructions and educational information\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EMemora Health [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref35\" rel=\"footnote\"\u003E35\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EProvides access to care instructions and educational information\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EAiCure [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref36\" rel=\"footnote\"\u003E36\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ECoaches patients to manage their condition and adhere to instructions\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EInfinity [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref37\" rel=\"footnote\"\u003E37\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EAssesses health outcomes and impact of phone-based monitoring for patients with cancer aged ≥65 years\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EVik [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref38\" rel=\"footnote\"\u003E38\u003C\u002Fa\u003E\u003C\u002Fspan\u003E,\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref39\" rel=\"footnote\"\u003E39\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EAddresses patients’ daily needs and concerns\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd colspan=\"5\" rowspan=\"1\"\u003E\u003Cb\u003ESupport\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003ECounseling\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EVivobot [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref40\" rel=\"footnote\"\u003E40\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ECognitive and behavioral intervention for positive psychology skills and promoting well-being\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003EEmotional support\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EYouper [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref26\" rel=\"footnote\"\u003E26\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EDaily emotional support and mental health tracking\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EWysa [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref26\" rel=\"footnote\"\u003E26\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EDaily emotional support and mental health tracking\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EReplika [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref26\" rel=\"footnote\"\u003E26\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EDaily emotional support and mental health tracking\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EUnmind [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref26\" rel=\"footnote\"\u003E26\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EDaily emotional support and mental health tracking\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EShim [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref26\" rel=\"footnote\"\u003E26\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EDaily emotional support and mental health tracking\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EWoebot [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref41\" rel=\"footnote\"\u003E41\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EDaily emotional support and mental health tracking\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd colspan=\"5\" rowspan=\"1\"\u003E\u003Cb\u003EWorkflow efficiency\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003EAdministration\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003ESense.ly [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref42\" rel=\"footnote\"\u003E42\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EAssists in monitoring appointments, manages patients’ conditions, and suggests therapies\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003ECareskore [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref42\" rel=\"footnote\"\u003E42\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ETracks vitals and anticipates the need for hospital admissions\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EMandy [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref43\" rel=\"footnote\"\u003E43\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EAssists health care staff by automating the patient intake process\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003EPatient encounter\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EHOLMeS [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref44\" rel=\"footnote\"\u003E44\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ESupports diagnosis, chooses the proper treatment pathway, and provides prevention check-ups\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd colspan=\"5\" rowspan=\"1\"\u003E\u003Cb\u003EHealth promotion\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003EGeneral lifestyle coaching\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003ESWITCHes [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref45\" rel=\"footnote\"\u003E45\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ETracks patients’ progress, provides insight to physicians, and suggests suitable activities\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003ECoachAI [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref46\" rel=\"footnote\"\u003E46\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ETracks patients’ progress, provides insight to physicians, and suggests suitable activities\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EWeightMentor [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref47\" rel=\"footnote\"\u003E47\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EProvides self-help motivation for weight loss maintenance and allows for open conversation\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003EHealthy eating\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EHealth Hero [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref48\" rel=\"footnote\"\u003E48\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EGuides in making informed decisions around food choices to change unhealthy eating habits\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003ETasteful Bot [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref48\" rel=\"footnote\"\u003E48\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EGuides in making informed decisions around food choices to change unhealthy eating habits\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EForksy [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref48\" rel=\"footnote\"\u003E48\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EGuides in making informed decisions around food choices to change unhealthy eating habits\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003ESLOWbot [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref49\" rel=\"footnote\"\u003E49\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003EGuides in making informed decisions around food choices to change unhealthy eating habits\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd colspan=\"4\" rowspan=\"1\"\u003E\u003Cb\u003ESmoking cessation\u003C\u002Fb\u003E\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003ESMAG [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref50\" rel=\"footnote\"\u003E50\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ECognitive behavioral therapy\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr valign=\"top\"\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003E\u003Cbr\u003E\u003C\u002Ftd\u003E\u003Ctd rowspan=\"1\" colspan=\"1\"\u003EBella [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref51\" rel=\"footnote\"\u003E51\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]\u003C\u002Ftd\u003E\u003Ctd colspan=\"2\" rowspan=\"1\"\u003ECoaches to help quit smoking\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003C\u002Ftbody\u003E\u003C\u002Ftable\u003E\u003C\u002Fdiv\u003E\u003Ch5\u003EDiagnostics and Screening\u003C\u002Fh5\u003E\u003Cp class=\"abstract-paragraph\"\u003EAn accurate diagnosis is critical for appropriate care to be administered. In terms of cancer diagnostics, AI-based computer vision is a function often used in chatbots that can recognize subtle patterns from images. This would increase physicians’ confidence when identifying cancer types, as even highly trained individuals may not always agree on the diagnosis [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref52\" rel=\"footnote\"\u003E52\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Studies have shown that the interpretation of medical images for the diagnosis of tumors performs equally well or better with AI compared with experts [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref53\" rel=\"footnote\"\u003E53\u003C\u002Fa\u003E\u003C\u002Fspan\u003E-\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref56\" rel=\"footnote\"\u003E56\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. In addition, automated diagnosis may be useful when there are not enough specialists to review the images. This was made possible through deep learning algorithms in combination with the increasing availability of databases for the tasks of detection, segmentation, and classification [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref57\" rel=\"footnote\"\u003E57\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. For example, Medical Sieve (IBM Corp) is a chatbot that examines radiological images to aid and communicate with cardiologists and radiologists to identify issues quickly and reliably [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref24\" rel=\"footnote\"\u003E24\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Similarly, InnerEye (Microsoft Corp) is a computer-assisted image diagnostic chatbot that recognizes cancers and diseases within the eye but does not directly interact with the user like a chatbot [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref42\" rel=\"footnote\"\u003E42\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Even with the rapid advancements of AI in cancer imaging, a major issue is the lack of a gold standard [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref58\" rel=\"footnote\"\u003E58\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EFrom the patient’s perspective, various chatbots have been designed for symptom screening and self-diagnosis. The ability of patients to be directed to urgent referral pathways through early warning signs has been a promising market. Decreased wait times in accessing health care services have been found to correlate with improved patient outcomes and satisfaction [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref59\" rel=\"footnote\"\u003E59\u003C\u002Fa\u003E\u003C\u002Fspan\u003E-\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref61\" rel=\"footnote\"\u003E61\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The automated chatbot, Quro (Quro Medical, Inc), provides presynopsis based on symptoms and history to predict user conditions (average precision approximately 0.82) without a form-based data entry system [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref25\" rel=\"footnote\"\u003E25\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. In addition to diagnosis, Buoy Health (Buoy Health, Inc) assists users in identifying the cause of their illness and provides medical advice [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref26\" rel=\"footnote\"\u003E26\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Another chatbot designed by Harshitha et al [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref27\" rel=\"footnote\"\u003E27\u003C\u002Fa\u003E\u003C\u002Fspan\u003E] uses dialog flow to provide an initial analysis of breast cancer symptoms. It has been proven to be 95% accurate in differentiating between normal and cancerous images. Even with promising results, there are still potential areas for improvement. A study of 3 mobile app–based chatbot symptom checkers, Babylon (Babylon Health, Inc), Your.md (Healthily, Inc), and Ada (Ada, Inc), indicated that sensitivity remained low at 33% for the detection of head and neck cancer [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref28\" rel=\"footnote\"\u003E28\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The number of studies assessing the development, implementation, and effectiveness are still relatively limited compared with the diversity of chatbots currently available. Further studies are required to establish the efficacy across various conditions and populations. Nonetheless, chatbots for self-diagnosis are an effective way of advising patients as the first point of contact if accuracy and sensitivity requirements can be satisfied.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EEarly cancer detection can lead to higher survival rates and improved quality of life. Inherited factors are present in 5% to 10% of cancers, including breast, colorectal, prostate, and rare tumor syndromes [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref62\" rel=\"footnote\"\u003E62\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Family history collection is a proven way of easily accessing the genetic disposition of developing cancer to inform risk-stratified decision-making, clinical decisions, and cancer prevention [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref63\" rel=\"footnote\"\u003E63\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The web-based chatbot ItRuns (ItRunsInMyFamily) gathers family history information at the population level to determine the risk of hereditary cancer [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref29\" rel=\"footnote\"\u003E29\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. We have yet to find a chatbot that incorporates deep learning to process large and complex data sets at a cellular level. Although not able to directly converse with users, DeepTarget [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref64\" rel=\"footnote\"\u003E64\u003C\u002Fa\u003E\u003C\u002Fspan\u003E] and deepMirGene [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref65\" rel=\"footnote\"\u003E65\u003C\u002Fa\u003E\u003C\u002Fspan\u003E] are capable of performing miRNA and target predictions using expression data with higher accuracy compared with non–deep learning models. With the advent of phenotype–genotype predictions, chatbots for genetic screening would greatly benefit from image recognition. New screening biomarkers are also being discovered at a rapid speed, so continual integration and algorithm training are required. These findings align with studies that demonstrate that chatbots have the potential to improve user experience and accessibility and provide accurate data collection [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref66\" rel=\"footnote\"\u003E66\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Ch5\u003ETreatment\u003C\u002Fh5\u003E\u003Cp class=\"abstract-paragraph\"\u003EChatbots are now able to provide patients with treatment and medication information after diagnosis without having to directly contact a physician. Such a system was proposed by Mathew et al [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref30\" rel=\"footnote\"\u003E30\u003C\u002Fa\u003E\u003C\u002Fspan\u003E] that identifies the symptoms, predicts the disease using a symptom–disease data set, and recommends a suitable treatment. Although this may seem as an attractive option for patients looking for a fast solution, computers are still prone to errors, and bypassing professional inspection may be an area of concern. Chatbots may also be an effective resource for patients who want to learn why a certain treatment is necessary. Madhu et al [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref31\" rel=\"footnote\"\u003E31\u003C\u002Fa\u003E\u003C\u002Fspan\u003E] proposed an interactive chatbot app that provides a list of available treatments for various diseases, including cancer. This system also informs the user of the composition and prescribed use of medications to help select the best course of action. The diagnosis and course of treatment for cancer are complex, so a more realistic system would be a chatbot used to connect users with appropriate specialists or resources. A text-to-text chatbot by Divya et al [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref32\" rel=\"footnote\"\u003E32\u003C\u002Fa\u003E\u003C\u002Fspan\u003E] engages patients regarding their medical symptoms to provide a personalized diagnosis and connects the user with the appropriate physician if major diseases are detected. Rarhi et al [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref33\" rel=\"footnote\"\u003E33\u003C\u002Fa\u003E\u003C\u002Fspan\u003E] proposed a similar design that provides a diagnosis based on symptoms, measures the seriousness, and connects users with a physician if needed [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref33\" rel=\"footnote\"\u003E33\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. In general, these systems may greatly help individuals in conducting daily check-ups, increase awareness of their health status, and encourage users to seek medical assistance for early intervention.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EChatbots have also been used by physicians during treatment planning. For example, IBM’s Watson for Oncology examines data from records and medical notes to generate an evidence-based treatment plan for oncologists [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref34\" rel=\"footnote\"\u003E34\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Studies have shown that Watson for Oncology still cannot replace experts at this moment, as quite a few cases are not consistent with experts (approximately 73% concordant) [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref67\" rel=\"footnote\"\u003E67\u003C\u002Fa\u003E\u003C\u002Fspan\u003E,\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref68\" rel=\"footnote\"\u003E68\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Nonetheless, this could be an effective decision-making tool for cancer therapy to standardize treatments. Although not specifically an oncology app, another chatbot example for clinicians’ use is the chatbot Safedrugbot (Safe In Breastfeeding) [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref69\" rel=\"footnote\"\u003E69\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. This is a chat messaging service for health professionals offering assistance with appropriate drug use information during breastfeeding. Promising progress has also been made in using AI for radiotherapy to reduce the workload of radiation staff or identify at-risk patients by collecting outcomes before and after treatment [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref70\" rel=\"footnote\"\u003E70\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. An ideal chatbot for health care professionals’ use would be able to accurately detect diseases and provide the proper course of recommendations, which are functions currently limited by time and budgetary constraints. Continual algorithm training and updates would be necessary because of the constant improvements in current standards of care. Further refinements and testing for the accuracy of algorithms are required before clinical implementation [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref71\" rel=\"footnote\"\u003E71\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. This area holds tremendous potential, as an estimated ≥50% of all patients with cancer have used radiotherapy during the course of their treatment.\u003C\u002Fp\u003E\u003Ch5\u003EPatient Monitoring\u003C\u002Fh5\u003E\u003Cp class=\"abstract-paragraph\"\u003EChatbots have been implemented in remote patient monitoring for postoperative care and follow-ups. The health care sector is among the most overwhelmed by those needing continued support outside hospital settings, as most patients newly diagnosed with cancer are aged ≥65 years [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref72\" rel=\"footnote\"\u003E72\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The integration of this application would improve patients’ quality of life and relieve the burden on health care providers through better disease management, reducing the cost of visits and allowing timely follow-ups. In terms of cancer therapy, remote monitoring can support patients by enabling higher dose chemotherapy drug delivery, reducing secondary hospitalizations, and providing health benefits after surgery [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref73\" rel=\"footnote\"\u003E73\u003C\u002Fa\u003E\u003C\u002Fspan\u003E-\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref75\" rel=\"footnote\"\u003E75\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EStreamMD (StreamMD, Inc), Conversa (Conversa Health, Inc), and Memora Health (Memora Health, Inc) are chatbots that function on existing messaging platforms that provide patients with immediate access to care instructions and educational information [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref35\" rel=\"footnote\"\u003E35\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. To ensure that patients adhere to instructions, AiCure (AiCure, Inc) uses a smartphone webcam to coach them in managing their condition. Recently, a chatbot architecture was proposed for patient support based on microservices to provide personalized eHealth functionalities and data storage [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref36\" rel=\"footnote\"\u003E36\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Several studies have supported the application of chatbots for patient monitoring [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref76\" rel=\"footnote\"\u003E76\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The semiautomized messaging chatbot Infinity (Facebook, Inc) was used to assess the health outcomes and health care impacts of phone-based monitoring for patients with cancer aged ≥65 years. After 2 years of implementation, there was a 97% satisfactory rate, and 87% considered monitoring useful, with the most reported benefit being treatment management and moral support [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref37\" rel=\"footnote\"\u003E37\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Similar results were discovered in 2 studies using Vik (WeFight, Inc), a text-based chatbot that responds to the daily needs and concerns of patients and their relatives with personal insights. A 1-year prospective study of 4737 patients with breast cancer reported a 94% overall satisfaction rate [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref38\" rel=\"footnote\"\u003E38\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. A more in-depth analysis of the 132,970 messages showed that users were more likely to answer multiple-choice questions compared with open-ended ones, chatbots improved treatment compliance rate by >20% (\u003Ci\u003EP\u003C\u002Fi\u003E=.04), and intimate or sensitive topics were openly discussed. An area of concern is that retention rates drastically decreased to 31% by the end of this study. The other study was a phase 3, blind, noninferiority randomized controlled trial (n=132) to assess the level of patient satisfaction with the answers provided by chatbots versus those by physicians [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref39\" rel=\"footnote\"\u003E39\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Using 12 frequently asked questions on breast cancer, participants were split into 2 groups to rate the quality of answers from chatbots or physicians. Among patients with breast cancer in treatment or remission, chatbot answers were shown to be noninferior (\u003Ci\u003EP\u003C\u002Fi\u003E<.001), with a success rate of 69% compared with 64% in the physician groups. Concerns regarding the chatbot’s ability to successfully answer more complex questions or detect differences between major and minor symptoms still remain to be addressed.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EFurther refinements and large-scale implementations are still required to determine the benefits across different populations and sectors in health care [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref26\" rel=\"footnote\"\u003E26\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Although overall satisfaction is found to be relatively high, there is still room for improvement by taking into account user feedback tailored to the patient’s changing needs during recovery. In combination with wearable technology and affordable software, chatbots have great potential to affect patient monitoring solutions.\u003C\u002Fp\u003E\u003Ch5\u003EPatient Support\u003C\u002Fh5\u003E\u003Cp class=\"abstract-paragraph\"\u003EThe prevalence of cancer is increasing along with the number of survivors of cancer, partly because of improved treatment techniques and early detection [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref77\" rel=\"footnote\"\u003E77\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. These individuals experience added health problems, such as infections, chronic diseases, psychological issues, and sleep disturbances, which often require specific needs that are not met by many practitioners (ie, medical, psychosocial, informational, and proactive contact) [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref78\" rel=\"footnote\"\u003E78\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. A number of these individuals require support after hospitalization or treatment periods. Maintaining autonomy and living in a self-sustaining way within their home environment is especially important for older populations [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref79\" rel=\"footnote\"\u003E79\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Implementation of chatbots may address some of these concerns, such as reducing the burden on the health care system and supporting independent living.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EWith psychiatric disorders affecting at least 35% of patients with cancer, comprehensive cancer care now includes psychosocial support to reduce distress and foster a better quality of life [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref80\" rel=\"footnote\"\u003E80\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The first chatbot was designed for individuals with psychological issues [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref9\" rel=\"footnote\"\u003E9\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]; however, they continue to be used for emotional support and psychiatric counseling with their ability to express sympathy and empathy [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref81\" rel=\"footnote\"\u003E81\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Health-based chatbots delivered through mobile apps, such as Woebot (Woebot Health, Inc), Youper (Youper, Inc), Wysa (Wysa, Ltd), Replika (Luka, Inc), Unmind (Unmind, Inc), and Shim (Shim, Inc), offer daily emotional support and mental health tracking [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref26\" rel=\"footnote\"\u003E26\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. A study performed on Woebot, developed based on cognitive behavioral therapy, showed that depressive symptoms were significantly reduced, and participants were more receptive than in traditional therapies [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref41\" rel=\"footnote\"\u003E41\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. This agreed with the Shim results, also using the same type of therapy, which showed that the intervention was highly engaging, improved well-being, and reduced stress [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref82\" rel=\"footnote\"\u003E82\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. When another chatbot was developed based on the structured association technique counseling method, the user’s motivation was enhanced, and stress was reduced [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref83\" rel=\"footnote\"\u003E83\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Similarly, a graph-based chatbot has been proposed to identify the mood of users through sentimental analysis and provide human-like responses to comfort patients [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref84\" rel=\"footnote\"\u003E84\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Vivobot (HopeLab, Inc) provides cognitive and behavioral interventions to deliver positive psychology skills and promote well-being. This psychiatric counseling chatbot was effective in engaging users and reducing anxiety in young adults after cancer treatment [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref40\" rel=\"footnote\"\u003E40\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The limitation to the abovementioned studies was that most participants were young adults, most likely because of the platform on which the chatbots were available. In addition, longer follow-up periods with larger and more diverse sample sizes are needed for future studies. Chatbots used for psychological support hold great potential, as individuals are more comfortable disclosing personal information when no judgments are formed, even if users could still discriminate their responses from that of humans [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref82\" rel=\"footnote\"\u003E82\u003C\u002Fa\u003E\u003C\u002Fspan\u003E,\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref85\" rel=\"footnote\"\u003E85\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Ch5\u003EWorkflow Efficiency\u003C\u002Fh5\u003E\u003Cp class=\"abstract-paragraph\"\u003EElectronic health records have improved data availability but also increased the complexity of the clinical workflow, contributing to ineffective treatment plans and uninformed management [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref86\" rel=\"footnote\"\u003E86\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. A streamlined process using ML techniques would allow clinicians to spend more time with patients by decreasing the time spent on data entry through the ease of documentation, exposing relevant patient information from the chart, automatically authorizing payment, or reducing medical errors [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref58\" rel=\"footnote\"\u003E58\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. For example, Mandy is a chatbot that assists health care staff by automating the patient intake process [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref43\" rel=\"footnote\"\u003E43\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Using a combination of data-driven natural language processing with knowledge-driven diagnostics, this chatbot interviews the patient, understands their chief complaints, and submits reports to physicians for further analysis [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref43\" rel=\"footnote\"\u003E43\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Similarly, Sense.ly (Sense.ly, Inc) acts as a web-based nurse to assist in monitoring appointments, managing patients’ conditions, and suggesting therapies. Another chatbot that reduces the burden on clinicians and decreases wait time is Careskore (CareShore, Inc), which tracks vitals and anticipates the need for hospital admissions [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref42\" rel=\"footnote\"\u003E42\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Chatbots have also been proposed to autonomize patient encounters through several advanced eHealth services. In addition to collecting data and providing bookings, Health OnLine Medical Suggestions or HOLMES (Wipro, Inc) interacts with patients to support diagnosis, choose the proper treatment pathway, and provide prevention check-ups [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref44\" rel=\"footnote\"\u003E44\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Although the use of chatbots in health care and cancer therapy has the potential to enhance clinician efficiency, reimbursement codes for practitioners are still lacking before universal implementation. In addition, studies will need to be conducted to validate the effectiveness of chatbots in streamlining workflow for different health care settings. Nonetheless, chatbots hold great potential to complement telemedicine by streamlining medical administration and autonomizing patient encounters.\u003C\u002Fp\u003E\u003Ch5\u003EHealth Promotion\u003C\u002Fh5\u003E\u003Cp class=\"abstract-paragraph\"\u003ESurvivors of cancer, particularly those who underwent treatment during childhood, are more susceptible to adverse health risks and medical complications. Consequently, promoting a healthy lifestyle early on is imperative to maintain quality of life, reduce mortality, and decrease the risk of secondary cancers [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref87\" rel=\"footnote\"\u003E87\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. According to the analysis from the web directory, health promotion chatbots are the most commonly available; however, most of them are only available on a single platform. Thus, interoperability on multiple common platforms is essential for adoption by various types of users across different age groups. In addition, voice and image recognition should also be considered, as most chatbots are still text based.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EHealthy diets and weight control are key to successful disease management, as obesity is a significant risk factor for chronic conditions. Chatbots have been incorporated into health coaching systems to address health behavior modifications. For example, CoachAI and Smart Wireless Interactive Health System used chatbot technology to track patients’ progress, provide insight to physicians, and suggest suitable activities [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref45\" rel=\"footnote\"\u003E45\u003C\u002Fa\u003E\u003C\u002Fspan\u003E,\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref46\" rel=\"footnote\"\u003E46\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Another app is Weight Mentor, which provides self-help motivation for weight loss maintenance and allows for open conversation without being affected by emotions [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref47\" rel=\"footnote\"\u003E47\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Health Hero (Health Hero, Inc), Tasteful Bot (Facebook, Inc), Forksy (Facebook, Inc), and SLOWbot (iaso heath, Inc) guide users to make informed decisions on food choices to change unhealthy eating habits [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref48\" rel=\"footnote\"\u003E48\u003C\u002Fa\u003E\u003C\u002Fspan\u003E,\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref49\" rel=\"footnote\"\u003E49\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The effectiveness of these apps cannot be concluded, as a more rigorous analysis of the development, evaluation, and implementation is required. Nevertheless, chatbots are emerging as a solution for healthy lifestyle promotion through access and human-like communication while maintaining anonymity.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EMost would assume that survivors of cancer would be more inclined to practice health protection behaviors with extra guidance from health professionals; however, the results have been surprising. Smoking accounts for at least 30% of all cancer deaths; however, up to 50% of survivors continue to smoke [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref88\" rel=\"footnote\"\u003E88\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The benefit of using chatbots for smoking cessation across various age groups has been highlighted in numerous studies showing improved motivation, accessibility, and adherence to treatment, which have led to increased smoking abstinence [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref89\" rel=\"footnote\"\u003E89\u003C\u002Fa\u003E\u003C\u002Fspan\u003E-\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref91\" rel=\"footnote\"\u003E91\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The cognitive behavioral therapy–based chatbot SMAG, supporting users over the Facebook social network, resulted in a 10% higher cessation rate compared with control groups [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref50\" rel=\"footnote\"\u003E50\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Motivational interview–based chatbots have been proposed with promising results, where a significant number of patients showed an increase in their confidence and readiness to quit smoking after 1 week [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref92\" rel=\"footnote\"\u003E92\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. No studies have been found to assess the effectiveness of chatbots for smoking cessation in terms of ethnic, racial, geographic, or socioeconomic status differences. Creating chatbots with prespecified answers is simple; however, the problem becomes more complex when answers are open. Bella, one of the most advanced text-based chatbots on the market advertised as a coach for adults, gets stuck when responses are not prompted [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref51\" rel=\"footnote\"\u003E51\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Therefore, the reaction to unexpected responses is still an area in progress. Given all the uncertainties, chatbots hold potential for those looking to quit smoking, as they prove to be more acceptable for users when dealing with stigmatized health issues compared with general practitioners [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref7\" rel=\"footnote\"\u003E7\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cbr\u003E\u003Ch3 class=\"navigation-heading h3-main-heading\" id=\"Discussion\" data-label=\"Discussion\"\u003EDiscussion\u003C\u002Fh3\u003E\u003Ch4\u003EChallenges and Limitations\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EAI and ML have advanced at an impressive rate and have revealed the potential of chatbots in health care and clinical settings. AI technology outperforms humans in terms of image recognition, risk stratification, improved processing, and 24\u002F7 assistance with data and analysis. However, there is no machine substitute for higher-level interactions, critical thinking, and ambiguity [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref93\" rel=\"footnote\"\u003E93\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Chatbots create added complexity that must be identified, addressed, and mitigated before their universal adoption in health care.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EHesitancy from physicians and poor adoption by patients is a major barrier to overcome, which could be explained by many of the factors discussed in this section. A cross-sectional web-based survey of 100 practicing physicians gathered the perceptions of chatbots in health care [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref6\" rel=\"footnote\"\u003E6\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Although a wide variety of beneficial aspects were reported (ie, management of health and administration), an equal number of concerns were present. Over 70% of physicians believe that chatbots cannot effectively care for all the patients’ needs, cannot display human emotion, cannot provide detailed treatment plans, and pose a risk if patients self-diagnose or do not fully comprehend their diagnosis. If the limitations of chatbots are better understood and mitigated, the fears of adopting this technology in health care may slowly subside. The \u003Ci\u003EDiscussion\u003C\u002Fi\u003E section ends by exploring the challenges and questions for health care professionals, patients, and policy makers.\u003C\u002Fp\u003E\u003Ch4\u003EMoral and Ethical Constraints\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EThe use of chatbots in health care presents a novel set of moral and ethical challenges that must be addressed for the public to fully embrace this technology. Issues to consider are privacy or confidentiality, informed consent, and fairness. Each of these concerns is addressed below. Although efforts have been made to address these concerns, current guidelines and policies are still far behind the rapid technological advances [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref94\" rel=\"footnote\"\u003E94\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EHealth care data are highly sensitive because of the risk of stigmatization and discrimination if the information is wrongfully disclosed. The ability of chatbots to ensure privacy is especially important, as vast amounts of personal and medical information are often collected without users being aware, including voice recognition and geographical tracking. The public’s lack of confidence is not surprising, given the increased frequency and magnitude of high-profile security breaches and inappropriate use of data [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref95\" rel=\"footnote\"\u003E95\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Unlike financial data that becomes obsolete after being stolen, medical data are particularly valuable, as they are not perishable. Privacy threats may break the trust that is essential to the therapeutic physician–patient relationship and inhibit open communication of relevant clinical information for proper diagnosis and treatment [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref96\" rel=\"footnote\"\u003E96\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EChatbots experience the \u003Ci\u003EBlack\u003C\u002Fi\u003E\u003Ci\u003EBox\u003C\u002Fi\u003E problem, which is similar to many computing systems programmed using ML that are trained on massive data sets to produce multiple layers of connections. Although they are capable of solving complex problems that are unimaginable by humans, these systems remain highly opaque, and the resulting solutions may be unintuitive. This means that the systems’ behavior is hard to explain by merely looking inside, and understanding exactly how they are programmed is nearly impossible. For both users and developers, transparency becomes an issue, as they are not able to fully understand the solution or intervene to predictably change the chatbot’s behavior [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref97\" rel=\"footnote\"\u003E97\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. With the novelty and complexity of chatbots, obtaining valid informed consent where patients can make their own health-related risk and benefit assessments becomes problematic [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref98\" rel=\"footnote\"\u003E98\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Without sufficient transparency, deciding how certain decisions are made or how errors may occur reduces the reliability of the diagnostic process. The \u003Ci\u003EBlack Box\u003C\u002Fi\u003E problem also poses a concern to patient autonomy by potentially undermining the shared decision-making between physicians and patients [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref99\" rel=\"footnote\"\u003E99\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The chatbot’s personalized suggestions are based on algorithms and refined based on the user’s past responses. The removal of options may slowly reduce the patient’s awareness of alternatives and interfere with free choice [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref100\" rel=\"footnote\"\u003E100\u003C\u002Fa\u003E\u003C\u002Fspan\u003E].\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EFinally, the issue of fairness arises with algorithm bias when data used to train and test chatbots do not accurately reflect the people they represent [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref101\" rel=\"footnote\"\u003E101\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. As the AI field lacks diversity, bias at the level of the algorithm and modeling choices may be overlooked by developers [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref102\" rel=\"footnote\"\u003E102\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. In a study using 2 cases, differences in prediction accuracy were shown concerning gender and insurance type for intensive care unit mortality and psychiatric readmissions [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref103\" rel=\"footnote\"\u003E103\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. On a larger scale, this may exacerbate barriers to health care for minorities or underprivileged individuals, leading to worse health outcomes. Identifying the source of algorithm bias is crucial for addressing health care disparities between various demographic groups and improving data collection.\u003C\u002Fp\u003E\u003Ch4\u003EChances for Errors\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EAlthough studies have shown that AI technologies make fewer mistakes than humans in terms of diagnosis and decision-making, they still bear inherent risks for medical errors [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref104\" rel=\"footnote\"\u003E104\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The interpretation of speech remains prone to errors because of the complexity of background information, accuracy of linguistic unit segmentation, variability in acoustic channels, and linguistic ambiguity with homophones or semantic expressions. Chatbots are unable to efficiently cope with these errors because of the lack of common sense and the inability to properly model real-world knowledge [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref105\" rel=\"footnote\"\u003E105\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Another factor that contributes to errors and inaccurate predictions is the large, noisy data sets used to train modern models because large quantities of high-quality, representative data are often unavailable [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref58\" rel=\"footnote\"\u003E58\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. In addition to the concern of accuracy and validity, addressing clinical utility and effectiveness of improving patients’ quality of life is just as important. With the increased use of diagnostic chatbots, the risk of overconfidence and overtreatment may cause more harm than benefit [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref99\" rel=\"footnote\"\u003E99\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. There is still clear potential for improved decision-making, as diagnostic deep learning algorithms were found to be equivalent to health care professionals in classifying diseases in terms of accuracy [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref106\" rel=\"footnote\"\u003E106\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. These issues presented above all raise the question of who is legally liable for medical errors. Avoiding responsibility becomes easier when numerous individuals are involved at multiple stages, from development to clinical applications [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref107\" rel=\"footnote\"\u003E107\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Although the law has been lagging and litigation is still a gray area, determining legal liability becomes increasingly pressing as chatbots become more accessible in health care.\u003C\u002Fp\u003E\u003Ch4\u003ERegulatory Considerations\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003ERegulatory standards have been developed to accommodate for rapid modifications and ensure the safety and effectiveness of AI technology, including chatbots. The US Food and Drug Administration has recognized the distinctiveness of chatbots compared with traditional medical devices by defining the software within the medical device category and has outlined its approach through the Digital Health Innovation Action Plan [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref108\" rel=\"footnote\"\u003E108\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. With the growing number of AI algorithms approved by the Food and Drug Administration, they opened public consultations for setting performance targets, monitoring performance, and reviewing when performance strays from preset parameters [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref102\" rel=\"footnote\"\u003E102\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. The American Medical Association has also adopted the Augmented Intelligence in Health Care policy for the appropriate integration of AI into health care by emphasizing the design approach and enhancement of human intelligence [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref109\" rel=\"footnote\"\u003E109\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. An area of concern is that chatbots are not covered under the Health Insurance Portability and Accountability Act; therefore, users’ data may be unknowingly sold, traded, and marketed by companies [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref110\" rel=\"footnote\"\u003E110\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. On the other hand, overregulation may diminish the value of chatbots and decrease the freedom for innovators. Consequently, balancing these opposing aspects is essential to promote benefits and reduce harm to the health care system and society.\u003C\u002Fp\u003E\u003Ch4\u003EFuture Directions\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EChatbots’ robustness of integrating and learning from large clinical data sets, along with its ability to seamlessly communicate with users, contributes to its widespread integration in various health care components. Given the current status and challenges of cancer care, chatbots will likely be a key player in this field’s continual improvement. More specifically, they hold promise in addressing the triple aim of health care by improving the quality of care, bettering the health of populations, and reducing the burden or cost of our health care system. Beyond cancer care, there is an increasing number of creative ways in which chatbots could be applicable to health care. During the COVID-19 pandemic, chatbots were already deployed to share information, suggest behavior, and offer emotional support. They have the potential to prevent misinformation, detect symptoms, and lessen the mental health burden during global pandemics [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref111\" rel=\"footnote\"\u003E111\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. At the global health level, chatbots have emerged as a socially responsible technology to provide equal access to quality health care and break down the barriers between the rich and poor [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref112\" rel=\"footnote\"\u003E112\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. To further advance medicine and knowledge, the use of chatbots in education for learning and assessments is crucial for providing objective feedback, personalized content, and cost-effective evaluations [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref113\" rel=\"footnote\"\u003E113\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. For example, the development of the Einstein app as a web-based physics teacher enables interactive learning and evaluations but is still far from being perfect [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref114\" rel=\"footnote\"\u003E114\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Given chatbots’ diverse applications in numerous aspects of health care, further research and interdisciplinary collaboration to advance this technology could revolutionize the practice of medicine.\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EOn the basis of the discussion above, the following features are general directions of future suggestions for improvements in chatbots within cancer care in no particular order of importance:\u003C\u002Fp\u003E\u003Col type=\"1\"\u003E\u003Cli class=\"spacey\"\u003EPatients with cancer may feel vulnerable or fear discrimination from employers or society [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref115\" rel=\"footnote\"\u003E115\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Security of sensitive information must be held to the highest standards, especially when personal health information is shared between providers and hospital systems.\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EAn increasing number of patients are bringing internet-based information to consultations that are not critically assessed for trustworthiness or credibility. If used correctly, the additional health information could enhance understanding, improve the ability to manage their conditions, and increase confidence during interaction with physicians [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref116\" rel=\"footnote\"\u003E116\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. Unfortunately, this is often not the case, and most patients are not adequately informed regarding the proper screening of information. Ways to address this challenge include promoting awareness and developing patient management guidelines. Chatbots also have the potential to become a key player in their ability to screen for credible information. They could help vulnerable individuals critically navigate web-based cancer information, especially for the older or more chronic populations that tend to be less technologically adept.\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003ECurrent applications of chatbots as computerized decision support systems for diagnosis and treatment are relatively limited. The targeted audience for most has been for patients’ use, and few are designed to aid physicians at the point of care. Medical Sieve and Watson for Oncology are the only chatbots found in our search that are designed specifically for clinicians. There are far more AI tools in the market to help with clinical decision-making without the ability to interact with users [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref117\" rel=\"footnote\"\u003E117\u003C\u002Fa\u003E\u003C\u002Fspan\u003E]. With the rapid data collection from electronic health records, real-time predictions, and links to clinical recommendations, adding chatbot functionalities to current decision aids will only improve patient-centered care and streamline the workflow for clinicians.\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EMore concrete evidence of high quality and accuracy across a broad range of conditions and populations entails more representative training data reflecting racial biases and developing peer-reviewed algorithms to reduce the \u003Ci\u003EBlack Box\u003C\u002Fi\u003E problem.\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EIntegration into the health care system, particularly with telemedicine, for seamless delivery from the beginning to the end does not mean replacing in-person care but rather complementing the health care workflow to ensure patients receive continuity and coordination of care.\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EReimbursement of chatbot services to physicians who decide to implement this technology into their practice will likely increase adoption rates. Organizations and health providers will likely profit because chatbots allow for a more efficient and reduced cost of delivery.\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EContinual training of chatbots as new knowledge is uncovered, such as symptom patterns or standard of care, is needed.\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EAs the Vik study found that users were more likely to respond to multiple-choice questions over open-ended ones [\u003Cspan class=\"footers\"\u003E\u003Ca class=\"citation-link\" href=\"#ref38\" rel=\"footnote\"\u003E38\u003C\u002Fa\u003E\u003C\u002Fspan\u003E], chatbot developers should move toward the choice with higher response rates. Studies, surveys, and focus groups should continue to be conducted to determine the best ways to converse with users.\u003C\u002Fli\u003E\u003Cli class=\"spacey\"\u003EUniversal adoption of various technical features, such as training with additional languages, image recognition, voice recognition, user feedback to improve services according to needs, access on multiple common platforms, and reacting to unexpected responses, need to be considered.\u003C\u002Fli\u003E\u003C\u002Fol\u003E\u003Cp class=\"abstract-paragraph\"\u003EThe ability to accurately measure performance is critical for continuous feedback and improvement of chatbots, especially the high standards and vulnerable individuals served in health care. Given that the introduction of chatbots to cancer care is relatively recent, rigorous evidence-based research is lacking. Standardized indicators of success between users and chatbots need to be implemented by regulatory agencies before adoption. Once the primary purpose is defined, common quality indicators to consider are the success rate of a given action, nonresponse rate, comprehension quality, response accuracy, retention or adoption rates, engagement, and satisfaction level. The ultimate goal is to assess whether chatbots positively affect and address the 3 aims of health care. Regular quality checks are especially critical for chatbots acting as decision aids because they can have a major impact on patients’ health outcomes.\u003C\u002Fp\u003E\u003Ch4\u003EReview Limitations\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EThe systematic literature review and chatbot database search includes a few limitations. The literature review and chatbot search were all conducted by a single reviewer, which could have potentially introduced bias and limited findings. In addition, our review explored a broad range of health care topics, and some areas could have been elaborated upon and explored more deeply. Furthermore, only a limited number of studies were included for each subtopic of chatbots for oncology apps because of the scarcity of studies addressing this topic. Future studies should consider refining the search strategy to identify other potentially relevant sources that may have been overlooked and assign multiple reviews to limit individual bias.\u003C\u002Fp\u003E\u003Ch4\u003EConclusions\u003C\u002Fh4\u003E\u003Cp class=\"abstract-paragraph\"\u003EAs illustrated in this review, these chatbots’ potential in cancer diagnostics and treatment, patient monitoring and support, clinical workflow efficiency, and health promotion have yet to be fully explored. Numerous risks and challenges will continue to arise that require careful navigation with the rapid advancements in chatbots. Consequently, weighing the gains versus threats with a critical eye is imperative. Even after laying down the proper foundations for using chatbots safely and effectively, the human element in the practice of medicine is irreplaceable and will always be present. Health care professionals have the responsibility of understanding both the benefits and risks associated with chatbots and, in turn, educating their patients.\u003C\u002Fp\u003E\u003C\u002Farticle\u003E\u003Cp\u003E\u003Ch4 class=\"h4-border-top\"\u003EAcknowledgments\u003C\u002Fh4\u003E\u003C\u002Fp\u003E\u003Cp class=\"abstract-paragraph\"\u003EThis work was supported by a Canadian Institutes of Health Research Planning and Dissemination Grant—Institute Community Support under grant number CIHR PCS-168296.\u003C\u002Fp\u003E\u003Ch4 class=\"h4-border-top\"\u003EConflicts of Interest\u003C\u002Fh4\u003E\u003Cp\u003E\u003Cp class=\"abstract-paragraph\"\u003ENone declared.\u003C\u002Fp\u003E\u003C\u002Fp\u003E\u003Cdiv class=\"footnotes\"\u003E\u003Ch4 id=\"References\" class=\"h4-border-top navigation-heading\" data-label=\"References\"\u003EReferences\u003C\u002Fh4\u003E\u003Col\u003E\u003Cli\u003E\u003Cspan id=\"ref1\"\u003EKersting K. 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[\u003Ca target=\"_blank\" href=\"https:\u002F\u002Fdx.doi.org\u002F10.1016\u002Fj.amepre.2011.01.014\"\u003ECrossRef\u003C\u002Fa\u003E]\u003C\u002Fspan\u003E\u003C\u002Fli\u003E\u003C\u002Fol\u003E\u003C\u002Fdiv\u003E\u003Cbr\u003E\u003Chr\u003E\u003Ca name=\"Abbreviations\"\u003E&lrm;\u003C\u002Fa\u003E\u003Ch4 class=\"navigation-heading\" id=\"Abbreviations\" data-label=\"Abbreviations\"\u003EAbbreviations\u003C\u002Fh4\u003E\u003Ctable width=\"80%\" border=\"0\" align=\"center\"\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EAI:\u003C\u002Fb\u003E artificial intelligence\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EML:\u003C\u002Fb\u003E machine learning\u003C\u002Ftd\u003E\u003C\u002Ftr\u003E\u003C\u002Ftable\u003E\u003Cbr\u003E\u003Chr\u003E\u003Cp style=\"font-style: italic\"\u003EEdited by G Eysenbach; submitted 09.02.21; peer-reviewed by M Falahee, H Turbe, S Ng; comments to author 10.06.21; revised version received 02.07.21; accepted 18.09.21; published 29.11.21\u003C\u002Fp\u003E\u003Ca href=\"https:\u002F\u002Fsupport.jmir.org\u002Fhc\u002Fen-us\u002Farticles\u002F115002955531\" id=\"Copyright\" target=\"_blank\" class=\"navigation-heading h4 d-block\" aria-label=\"Copyright - what is a Creative Commons License?\" data-label=\"Copyright\"\u003ECopyright \u003Cspan class=\"fas fa-question-circle\"\u003E\u003C\u002Fspan\u003E\u003C\u002Fa\u003E\u003Cp class=\"article-copyright\"\u003E©Lu Xu, Leslie Sanders, Kay Li, James C L Chow. Originally published in JMIR Cancer (https:\u002F\u002Fcancer.jmir.org), 29.11.2021.\u003C\u002Fp\u003E\u003Csmall class=\"article-license\"\u003E\u003Cp class=\"abstract-paragraph\"\u003EThis is an open-access article distributed under the terms of the Creative Commons Attribution License (https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on https:\u002F\u002Fcancer.jmir.org\u002F, as well as this copyright and license information must be included.\u003C\u002Fp\u003E\u003C\u002Fsmall\u003E\u003Cbr\u003E\u003C\u002Fsection\u003E\u003C\u002Farticle\u003E\u003C\u002Fsection\u003E\u003C\u002Fsection\u003E\u003C\u002Fmain\u003E\n"}],fetch:{},error:a,state:{host:a,environment:c,journalPath:o,keys:{},domains:{},screensize:"desktop",defaultCookieScriptId:"aa022ba6-b337-11ef-b288-3fb59f57942d",accessibility:{filter:"none","font-weight":"inherit","font-size":.625,"text-align":"initial"},announcements:{data:[{announcement_id:503,title:"JMIR Cancer Accepted for MEDLINE Indexing",description_short:"\u003Cp style=\"box-sizing: inherit; line-height: 2rem; color: rgb(26, 37, 76); font-family: Roboto, sans-serif;\"\u003EWe are pleased to announce that \u003Cem style=\"box-sizing: inherit;\"\u003EJMIR Cancer\u003C\u002Fem\u003E\u003Cem style=\"box-sizing: inherit;\"\u003E\u003Cspan style=\"box-sizing: inherit;\"\u003E \u003C\u002Fspan\u003E \u003C\u002Fem\u003Ehas been accepted for inclusion in MEDLINE, which is the U.S. National Library of Medicine's premier bibliographic database.\u003C\u002Fp\u003E\u003Cp style=\"box-sizing: inherit; line-height: 2rem; color: rgb(26, 37, 76); font-family: Roboto, sans-serif;\"\u003E\u003Cbr\u003E\u003C\u002Fp\u003E",date_posted:"2024-08-30T08:43:24.000Z",journal_id:f},{announcement_id:484,title:"JMIR Cancer Receives a Journal Impact Factor of 3.3",description_short:"\u003Cp\u003EJMIR Publications is pleased to announce that \u003Cem\u003EJMIR Cancer\u003C\u002Fem\u003E has received a Journal Impact Factor (JIF) of 3.3 as published in the 2024 Journal Citation Report (JCR) from Clarivate.\u003C\u002Fp\u003E",date_posted:"2024-06-27T08:57:49.000Z",journal_id:f},{announcement_id:381,title:"JMIR Cancer Receives Inaugural Impact Factor of 2.8",description_short:"\u003Cp\u003E\u003Cspan style=\"color: rgb(0, 0, 0); font-size: 11pt; font-family: Arial, sans-serif;\"\u003EJMIR Publications is pleased to announce that \u003Cem\u003EJMIR Cancer,\u003C\u002Fem\u003E\u003C\u002Fspan\u003E\u003Cspan style=\"color: rgb(0, 0, 0); font-family: "Times New Roman", serif;\"\u003E \u003C\u002Fspan\u003E\u003Cspan style=\"color: rgb(0, 0, 0); font-size: 11pt; font-family: Arial, sans-serif;\"\u003Eindexed in\u003C\u002Fspan\u003E\u003Cspan style=\"color: rgb(0, 0, 0); font-family: Calibri, sans-serif;\"\u003E \u003C\u002Fspan\u003E\u003Cspan style=\"color: rgb(0, 0, 0); font-size: 11pt; font-family: Arial, sans-serif;\"\u003E\u003Cspan style=\"color: black; text-decoration-line: none;\"\u003EEmerging Sources Citation Index\u003C\u002Fspan\u003E (ESCI), has been given an inaugural Journal Impact Factor (JIF) of 2.8 from the 2023 Journal Citation Reports™ (JCR).\u003C\u002Fspan\u003E\u003C\u002Fp\u003E",date_posted:"2023-06-28T15:58:38.000Z",journal_id:f},{announcement_id:345,title:"JMIR Cancer Expected to Receive Impact Factor in 2023",description_short:"\u003Cp style=\"box-sizing: inherit; line-height: 2rem; color: rgb(26, 37, 76); font-family: Roboto, sans-serif;\"\u003E\u003Cspan style=\"color: rgb(17, 17, 17); font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003EThe JMIR Cancer has been indexed by Clarivate since 2015 and will receive its' first Impact Factor in 2023\u003C\u002Fspan\u003E\u003C\u002Fp\u003E",date_posted:"2022-07-29T15:10:03.000Z",journal_id:f},{announcement_id:258,title:"JMIR Cancer Receives Prestigious DOAJ Seal",description_short:"\u003Cp\u003EJMIR Publications is happy to announce that JMIR Cancer has been awarded the prestigious Directory of Open Access Journals (DOAJ) Seal. The DOAJ Seal is awarded to journals that demonstrate best practice in open access publishing. Only 10% of the 15,000 peer-reviewed journals indexed in DOAJ have been awarded this Seal. \u003C\u002Fp\u003E",date_posted:"2021-04-14T17:02:17.000Z",journal_id:f},{announcement_id:ag,title:"JMIR Cancer Now Indexed in Scopus",description_short:"\u003Cp\u003E(Toronto, May 12, 2020) We are happy to announce that JMIR Cancer, which published its first article in 2015, has been accepted for inclusion in Elsevier’s Scopus.\u003C\u002Fp\u003E\r\n\r\n\u003Cp\u003EJMIR Publications is pleased to have the following journals indexed\u002Faccepted in Scopus:\u003C\u002Fp\u003E\u003Cul\u003E\u003Cli\u003E\r\nJournal of Medical Internet Research\u003C\u002Fli\u003E\u003Cli\u003EJMIR mHealth & uHealth \u003C\u002Fli\u003E\u003Cli\u003EJMIR Research Protocols \u003C\u002Fli\u003E\u003Cli\u003EJMIR Human Factors \u003C\u002Fli\u003E\u003Cli\u003EJMIR Mental Health \u003C\u002Fli\u003E\u003Cli\u003EJMIR Serious Games \u003C\u002Fli\u003E\u003Cli\u003EJMIR Medical Informatics \u003C\u002Fli\u003E\u003Cli\u003EJMIR Formative Research\u003C\u002Fli\u003E\u003Cli\u003EJMIR Diabetes\u003C\u002Fli\u003E\u003Cli\u003EJMIR Cancer\u003C\u002Fli\u003E\u003Cli\u003EJMIR Pediatrics and Parenting\u003C\u002Fli\u003E\u003Cli\u003EJMIR Medical Education\u003C\u002Fli\u003E\u003Cli\u003EJMIR Aging\u003C\u002Fli\u003E\u003Cli\u003EJMIR Rehabilitation and Assistive Technologies\u003C\u002Fli\u003E\u003Cli\u003EJMIR Cardio\u003C\u002Fli\u003E\u003Cli\u003EJMIR Public Health and Surveillance\u003C\u002Fli\u003E\u003Cli\u003EJournal of Participatory Medicine\u003C\u002Fli\u003E\u003C\u002Ful\u003E\u003Cp\u003EAdditional journals may currently be under evaluation.\u003C\u002Fp\u003E\u003Cp\u003EScopus is used by more than 5,000 institutions worldwide and JMIR Publications is glad to be a part of it. \u003C\u002Fp\u003E\u003Cp\u003EKnowledge base article: \u003Ca href=\"https:\u002F\u002Fsupport.jmir.org\u002Fhc\u002Fen-us\u002Farticles\u002F360007270972-Which-JMIR-journals-are-indexed-in-Scopus-\"\u003EWhich JMIR journals are indexed in Scopus?\u003C\u002Fa\u003E\u003C\u002Fp\u003E",date_posted:"2020-05-12T15:14:13.000Z",journal_id:f},{announcement_id:140,title:"JMIR Cancer accepted for PMC and PubMed",description_short:"\u003Cp\u003E\u003Cem\u003EUpdate Apr 17, 2017: This process is finalized, JMIR Cancer can now be found in \u003Ca href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpubmed?term=%22JMIR+Cancer%22[jour]\" target=\"_blank\"\u003EPubMed\u003C\u002Fa\u003E and in \u003Ca href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Fjournals\u002F3180\u002F\" target=\"_blank\"\u003EPubMed Central\u003C\u002Fa\u003E.\u003C\u002Fem\u003E\u003C\u002Fp\u003E\r\n\u003Cp\u003E(Toronto, Jan 31st, 2017) We are pleased to announce that \u003Cem\u003EJMIR Cancer\u003C\u002Fem\u003E (JC) has been accepted by the scientific evaluators at NCBI for indexing in PubMed Central and PubMed. The staff at PubMed Central is currently checking our XML files and finalizing the setup and we expect all articles published since Vol 1 \u002F Iss 1 to be in PubMed Central and PubMed within the next couple of months. The acceptance is the result of a rigorous scientific and technical evaluation by the US National Library of Medicine's (NLM’s) Library Operations Division, which decided that the scientific and editorial character and quality of \u003Cem\u003EJMIR Cancer\u003C\u002Fem\u003E merits its inclusion in PMC. In making this decision NLM has considered the suitability of the journal for the NLM collection as well as the opinions of expert consultants. \u003C\u002Fp\u003E\r\n\u003Cp\u003E\u003Cem\u003EJMIR Cancer\u003C\u002Fem\u003E is the 12th journal of JMIR Publications accepted for PMC\u002FPubmed. Other titles already in PMC\u002FPubmed include the \u003Cem\u003EJournal of Medical Internet Research\u003C\u002Fem\u003E (JMIR),\u003Cem\u003E interactive Journal of Medical Research\u003C\u002Fem\u003E (i-JMR), \u003Cem\u003EJMIR Research Protocols\u003C\u002Fem\u003E, \u003Cem\u003EJMIR mHealth and uHealth\u003C\u002Fem\u003E, \u003Cem\u003EJMIR Medical Informatics\u003C\u002Fem\u003E, \u003Cem\u003EJMIR Serious Games\u003C\u002Fem\u003E, \u003Cem\u003EJMIR Mental Health, JMIR Human Factors, JMIR Public Health and Surveillance, JMIR Medical Education\u003C\u002Fem\u003E and \u003Cem\u003EMedicine 2.0\u003C\u002Fem\u003E. In addition,\u003Cem\u003E \u003Cem\u003EJMIR Rehabilitation and Assistive Technologies\u003C\u002Fem\u003E is\u003C\u002Fem\u003E currently under evaluation, and \u003Cem\u003E\u003Cem\u003EJMIR Diabetes, JMIR Biomed Eng and \u003Cem\u003E\u003Cem\u003EJMIR Cardio \u003C\u002Fem\u003E\u003C\u002Fem\u003E\u003C\u002Fem\u003E\u003C\u002Fem\u003Ewill be submitted shortly.\u003C\u002Fp\u003E\r\n\u003Cp\u003EWe are still seeking academic leaders in the field of technology in oncology, cancer survivorship and cancer education and related fields to apply as \u003Cstrong\u003Esection editors.\u003C\u002Fstrong\u003E There will be remuneration in form of a credit system, rewarding actions such as taking on papers as submission editor. EB members will also be able to use a small discount for their own papers, or papers they invite from other authors. \u003C\u002Fp\u003E\r\n\u003Cp\u003EPrequisites include a scholarly track-record, demonstrated by being a first author on peer-reviewed publications and having served as peer-reviewer (preferably this should include JMIR journals. Applicants can self-assign themselves to papers to be peer-reviewed at \u003Ca href=\"http:\u002F\u002Fpreprints.jmir.org\u002F\"\u003EJMIR Preprints\u003C\u002Fa\u003E). \u003C\u002Fp\u003E\r\n\u003Cp\u003EPlease read our FAQ article\u003C\u002Fp\u003E\r\n\u003Cp\u003E\u003C\u002Fp\u003E\r\n\u003Cul class=\"src-component-helpCenter-HelpCenterResults-list src-component-helpCenter-HelpCenterResults-listBottom src-styles-utils-u-paddingBM \"\u003E\r\n\u003Cul class=\"src-component-helpCenter-HelpCenterResults-list src-component-helpCenter-HelpCenterResults-listBottom src-styles-utils-u-paddingBM \"\u003E\r\n\u003Cul class=\"src-component-helpCenter-HelpCenterResults-list src-component-helpCenter-HelpCenterResults-listBottom src-styles-utils-u-paddingBM \"\u003E\r\n\u003Cul class=\"src-component-helpCenter-HelpCenterResults-list src-component-helpCenter-HelpCenterResults-listBottom src-styles-utils-u-paddingBM \"\u003E\r\n\u003Cli class=\"src-component-helpCenter-HelpCenterResults-item \"\u003E\u003Ca class=\"u-userTextColor\" href=\"https:\u002F\u002Fjmir.zendesk.com\u002Fhc\u002Fen-us\u002Farticles\u002F115001347207-How-can-I-become-an-editorial-board-member-\" target=\"_blank\"\u003EHow can I become an editorial board member?\u003C\u002Fa\u003E\u003C\u002Fli\u003E\r\n\u003C\u002Ful\u003E\r\n\u003C\u002Ful\u003E\r\n\u003C\u002Ful\u003E\r\n\u003C\u002Ful\u003E\r\n\u003Cp\u003E\u003C\u002Fp\u003E\r\n\u003Cp\u003Eand apply at \u003Ca href=\"http:\u002F\u002Ftinyurl.com\u002Fjmir-eb-appl\" title=\"application form for EB\" target=\"_blank\"\u003Ehttp:\u002F\u002Ftinyurl.com\u002Fjmir-eb-appl\u003C\u002Fa\u003E\u003C\u002Fp\u003E",date_posted:"2017-01-31T13:50:54.000Z",journal_id:f},{announcement_id:123,title:"JMIR Cancer seeking Editorial Board and Editor-in-Chief nominations",description_short:"\u003Cp\u003EWe are currently seeking academic leaders in this field to apply as \u003Cstrong\u003Esection editors\u003C\u002Fstrong\u003E for Editorial Board positions for JMIR Cancer including the \u003Cstrong\u003EEditor-in-Chief\u003C\u002Fstrong\u003E. There will be remuneration in form of a credit system, rewarding actions such as taking on papers as submission editor. EB members will also be able to use a small discount for their own papers, or papers they invite from other authors. \u003C\u002Fp\u003E\r\n\u003Cp\u003EPrequisites include a scholarly track-record, demonstrated by being a first author on peer-reviewed publications and having served as peer-reviewer (preferably this should include JMIR journals. Applicants can self-assign themselves to papers to be peer-reviewed at \u003Ca href=\"http:\u002F\u002Fpreprints.jmir.org\u002F\"\u003EJMIR Preprints\u003C\u002Fa\u003E). \u003C\u002Fp\u003E\r\n\u003Cp\u003EPlease apply at \u003Ca href=\"http:\u002F\u002Ftinyurl.com\u002Fjmir-eb-appl\" title=\"application form for EB\" target=\"_blank\"\u003Ehttp:\u002F\u002Ftinyurl.com\u002Fjmir-eb-appl\u003C\u002Fa\u003E\u003C\u002Fp\u003E",date_posted:"2016-02-05T14:24:35.000Z",journal_id:f},{announcement_id:113,title:"How JMIR is maximizing dissemination and impact of your research",description_short:"\u003Cp\u003EJMIR Publications is proud to partner with TrendMD, offering JMIR authors additional visibility for their articles, displaying their articles across the TrendMD publisher network, which includes publishers such as BMJ or the JAMA network. \u003C\u002Fp\u003E\r\n\u003Cp\u003EIn addition to \"organic\" displays, JMIR authors can also pay for additional dissemination packages on TrendMD or Twitter ($99 each), which drives traffic and citations. See JMIR Research Dissemination Partnership with TrendMD (\u003Ca href=\"http:\u002F\u002Fwww.jmir.org\u002Fcontent\u002Ftrendmd\"\u003Ehttp:\u002F\u002Fwww.jmir.org\u002Fcontent\u002Ftrendmd\u003C\u002Fa\u003E) for additional information.\u003C\u002Fp\u003E",date_posted:"2015-12-04T14:16:05.000Z",journal_id:f},{announcement_id:94,title:"New JMIR journals - no submission or publication fees!",description_short:"\u003Cp\u003E\u003Cem\u003E(Update Jun 2nd, 2015: This is an older posting, and some of the journals below including those marked with * now have Article Processing Fees. Please make sure to check the \u003Ca href=\"https:\u002F\u002Fcancer.jmir.org\u002Ffees\u002Farticle-processing-fees\"\u003EFee Schedule\u003C\u002Fa\u003E). \u003C\u002Fem\u003E\u003C\u002Fp\u003E\r\n\u003Cp\u003E\u003Cem\u003E\u003C\u002Fem\u003EWe are pleased to announce our forthcoming new journals, all of which have currently no submission or publication fees, and all of which focus on emerging technologies and patient-centered innovations in specific areas, going beyond Internet\u002Fwebbased interventions: * JMIR Cancer (http:\u002F\u002Fcancer.jmir.org) * JMIR Medical Education (http:\u002F\u002Fmededu.jmir.org) * JMIR Public Health and Surveillance (http:\u002F\u002Fpublichealth.jmir.org) We welcome submissions for the inaugural issues of these journals. The following journals have already published articles and are still free of charge to publish in (no submission or publication fees): * JMIR Human Factors (http:\u002F\u002Fhumanfactors.jmir.org) * JMIR Rehabilitation and Assistive Technologies (http:\u002F\u002Frehab.jmir.org) * JMIR Mental Health (http:\u002F\u002Fmentalhealth.jmir.org) To submit to these journals, simply append \u002Fauthor to the URLs above, or submit to the main JMIR journal and use the dropdown-box in step 1 to change the journal name. All journals offer careful copyediting and typesetting of manuscripts, and submission to PubMed and PubMed Central (being new journals it may however take a few month until they appear in PubMed). We are also happy to announce that JMIR Medical Informatics and JMIR Serious Games are now indexed in PubMed.\u003C\u002Fp\u003E",date_posted:"2015-02-10T18:04:08.000Z",journal_id:f}],pagination:{from:b,to:p,total:p,perPage:p,firstPage:b,lastPage:b}},article:{data:{article_id:27850,published_at:"2021-11-29T09:15:04.000Z",submitted_at:ah,section_id:ai,journal_id:f,year:aj,issue:ak,volume:q,identifier:"27850",url:al,pdf_url:"https:\u002F\u002Fcancer.jmir.org\u002F2021\u002F4\u002Fe27850\u002FPDF",html_url:"https:\u002F\u002Fcancer.jmir.org\u002F2021\u002F4\u002Fe27850",xml_url:"https:\u002F\u002Fcancer.jmir.org\u002F2021\u002F4\u002Fe27850\u002FXML",title:"Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review",public_id:"JMIR Cancer 2021;7(4):e27850",thumbnail:"https:\u002F\u002Fasset.jmir.pub\u002Fassets\u002F1f6273f749f7b0fa0b4f42b4feaed538.png",doi:"10.2196\u002F27850",pmid:34847056,pmcid:"8669585",issue_title:"Oct-Dec",pages:[],transfer:a,authors:[{first_name:"Lu",last_name:"Xu",degrees:"MEng",deceased:a,orcid:"0000-0002-8485-9968",equal_contrib:v,matchedAffiliations:[b,e]},{first_name:"Leslie",last_name:"Sanders",degrees:w,deceased:a,orcid:"0000-0002-6990-7722",equal_contrib:v,matchedAffiliations:[x]},{first_name:"Kay",last_name:"Li",degrees:w,deceased:a,orcid:"0000-0002-5765-1635",equal_contrib:v,matchedAffiliations:[y]},{first_name:am,last_name:an,degrees:w,deceased:a,orcid:"0000-0003-4202-4855",equal_contrib:v,matchedAffiliations:[r,z]}],affiliations:[{aff_id:150676,author_id:ao,phone:a,fax:d,corresp_aff:s,aff_type:a,seq:b,article_id:a,institution_line_1:"Institute of Biomedical Engineering",institution_line_2:ap,institution_line_3:d,address_line_1:a,address_line_2:a,city:j,prov_state:g,postal_code:a,country:h},{aff_id:151063,author_id:ao,phone:a,fax:d,corresp_aff:s,aff_type:a,seq:e,article_id:a,institution_line_1:"Department of Medical Biophysics",institution_line_2:"Western University",institution_line_3:d,address_line_1:a,address_line_2:a,city:"London",prov_state:g,postal_code:a,country:h},{aff_id:150670,author_id:275455,phone:a,fax:d,corresp_aff:s,aff_type:a,seq:b,article_id:a,institution_line_1:"Department of Humanities",institution_line_2:aq,institution_line_3:d,address_line_1:a,address_line_2:a,city:j,prov_state:g,postal_code:a,country:h},{aff_id:150672,author_id:275456,phone:a,fax:d,corresp_aff:s,aff_type:a,seq:b,article_id:a,institution_line_1:"Department of English",institution_line_2:aq,institution_line_3:d,address_line_1:a,address_line_2:a,city:j,prov_state:g,postal_code:a,country:h},{aff_id:116775,author_id:ar,phone:as,fax:at,corresp_aff:b,aff_type:a,seq:b,article_id:a,institution_line_1:au,institution_line_2:av,institution_line_3:aw,address_line_1:ax,address_line_2:d,city:j,prov_state:g,postal_code:ay,country:h},{aff_id:151062,author_id:ar,phone:a,fax:d,corresp_aff:s,aff_type:a,seq:e,article_id:a,institution_line_1:"Department of Radiation Oncology",institution_line_2:ap,institution_line_3:d,address_line_1:a,address_line_2:a,city:j,prov_state:g,postal_code:a,country:h}],primaryAuthor:{first_name:am,last_name:an,email:"james.chow@rmp.uhn.ca",degrees:w,primaryAffiliation:{fax:at,phone:as,country:h,postal_code:ay,prov_state:g,city:j,address_line_1:ax,address_line_2:d,institution_line_1:au,institution_line_2:av,institution_line_3:aw}},abstract:"Background: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility.\nObjective: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation.\nMethods: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion.\nResults: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored.\nConclusions: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.\n",keywords:"ethics; artificial intelligence; medicine; machine learning; health; communication; mobile phone; diagnosis; chatbot; cancer therapy; medical biophysics",date_submitted:ah,title_html:a,sections:[{title:"Reviews on Innovations in Cancer",section_id:ai,journal_id:f,colour:k,count:G},{title:"Chatbots and Conversational Agents",section_id:763,journal_id:b,colour:A,count:573},{title:"Machine Learning",section_id:500,journal_id:q,colour:az,count:1584},{title:"Artificial Intelligence",section_id:797,journal_id:b,colour:A,count:1547},{title:"Cancer Self-Management",section_id:aA,journal_id:f,colour:k,count:58},{title:"Emotional, Social, Psychological Support for Cancer",section_id:341,journal_id:f,colour:k,count:208},{title:"Innovations and Technology in Cancer Care",section_id:297,journal_id:f,colour:k,count:441},{title:"Ethics, Privacy, and Legal Issues",section_id:aB,journal_id:b,colour:A,count:aA}],preprint:i,articleKD:F,isOldOjphiMigrated:F}},articles:{recent:[],openReview:[]},articleTypes:{},authentication:{data:a,jwt:a},countries:{data:[]},departments:{data:[]},help:{data:{}},journal:{data:{journal_id:f,title:aC,tag:aD,description:d,path:o,slug:o,seq:t,enabled:b,environment:c,url:aE,batch:e,year:l,colour:k,impact:H,order:I,published:aF,transfers:a,cite_score:aG,settings:{aboutJournal:"\u003Cp\u003E\u003Cem\u003EJMIR Cancer\u003C\u002Fem\u003E (JC, ISSN: 2369-1999) is a peer-reviewed journal focusing on education, innovation and technology in cancer care, cancer survivorship and cancer research, and participatory and patient-centred approaches. This journal also includes research on non-Internet approaches to improve cancer care and cancer research.\u003C\u002Fp\u003E\r\n\u003Cp\u003EWe invite submissions of original research, viewpoints, reviews, tutorials, case studies, and non-conventional articles (e.g. open patient education material and software resources that are not yet evaluated but are free for others to use\u002Fimplement). \u003C\u002Fp\u003E\r\n\u003Cp\u003EIn our \"Patients' Corner,\" we invite patients and survivors to submit short essays and viewpoints on all aspects of cancer. In particular, we are interested in suggestions on improving the health care system and suggestions for new technologies, applications and approaches (this section has no article processing fees).\u003C\u002Fp\u003E\r\n\u003Cp\u003EIn 2024, \u003Cem\u003EJMIR Cancer\u003C\u002Fem\u003E received a \u003Cstrong\u003E\u003Ca href=\"..\u002F..\u002F..\u002F..\u002F..\u002Fannouncements\u002F484\"\u003EJournal Impact Factor™ of 3.3\u003C\u002Fa\u003E\u003C\u002Fstrong\u003E (Source: Journal Citation Reports™ from Clarivate, 2024). \u003Cem\u003EJMIR Cancer\u003Cspan\u003E \u003C\u002Fspan\u003E\u003C\u002Fem\u003Eis indexed in \u003Ca href=\"..\u002F..\u002F..\u002F..\u002F..\u002Fannouncements\u002F140\"\u003EPubMed Central and PubMed\u003C\u002Fa\u003E, \u003Ca href=\"..\u002F..\u002F..\u002F..\u002F..\u002Fannouncements\u002F211\"\u003EScopus\u003C\u002Fa\u003E, \u003Ca href=\"https:\u002F\u002Fdoaj.org\u002Ftoc\u002F2369-1999?source=%7B%22query%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22terms%22%3A%7B%22index.issn.exact%22%3A%5B%222369-1999%22%5D%7D%7D%5D%7D%7D%2C%22size%22%3A100%2C%22sort%22%3A%5B%7B%22created_date%22%3A%7B%22order%22%3A%22desc%22%7D%7D%5D%2C%22_source%22%3A%7B%7D%2C%22track_total_hits%22%3Atrue%7D\"\u003EDOAJ\u003C\u002Fa\u003E, \u003Ca href=\"..\u002F..\u002F..\u002F..\u002FeditAnnouncement\u002F503\"\u003EMEDLINE\u003C\u002Fa\u003E, and the \u003Ca href=\"https:\u002F\u002Fmjl.clarivate.com\u002Fsearch-results?issn=2369-1999&hide_exact_match_fl=true&utm_source=mjl&utm_medium=share-by-link&utm_campaign=search-results-share-this-journal\"\u003EEmerging Sources Citation Index (Clarivate)\u003C\u002Fa\u003E. With a CiteScore of 4.1, JMIR Cancer is a Q2 journal in the field of Oncology, according to Scopus data.\u003C\u002Fp\u003E",announcementLink:"https:\u002F\u002Fcancer.jmir.org\u002Fannouncements\u002F484",copyrightNotice:"Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F2.0\u002F), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work (\"first published in the Journal of Medical Internet Research...\") is properly cited with original URL and bibliographic citation information. The complete bibliographic information, a link to the original publication on http:\u002F\u002Fwww.jmir.org\u002F, as well as this copyright and license information must be included.",focusScopeDesc:"\u003Cp\u003E\u003Cem\u003EJMIR Cancer\u003C\u002Fem\u003E \u003Cspan\u003E(JC, ISSN: 2369-1999\u003C\u002Fspan\u003E\u003Cspan\u003E) \u003C\u002Fspan\u003Eis a peer-reviewed journal focusing on education, innovation and technology in cancer care, cancer survivorship and cancer research, and participatory and patient-centred approaches. This journal also includes research on non-Internet approaches to improve cancer care and cancer research. In June 2024, \u003Cem\u003EJC\u003C\u002Fem\u003E \u003Cspan\u003Ereceived a \u003Cstrong\u003E\u003Ca href=\"..\u002F..\u002Fannouncements\u002F484\"\u003EImpact Factor of 3.3\u003C\u002Fa\u003E\u003C\u002Fstrong\u003E (5-Year Journal Impact Factor: 3.1) \u003C\u002Fspan\u003E\u003Cspan\u003Ein Clarivate’s Journal Citation Reports\u003C\u002Fspan\u003E™. \u003C\u002Fp\u003E\r\n\u003Cp\u003E\u003Cem\u003EJMIR Cancer\u003C\u002Fem\u003E is indexed in \u003Ca href=\"..\u002F..\u002Fannouncements\u002F140\"\u003EPubMed Central and PubMed\u003C\u002Fa\u003E, \u003Ca href=\"..\u002F..\u002Fannouncements\u002F211\"\u003EScopus\u003C\u002Fa\u003E, \u003Ca href=\"https:\u002F\u002Fdoaj.org\u002Ftoc\u002F2369-1999?source=%7B%22query%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22terms%22%3A%7B%22index.issn.exact%22%3A%5B%222369-1999%22%5D%7D%7D%5D%7D%7D%2C%22size%22%3A100%2C%22sort%22%3A%5B%7B%22created_date%22%3A%7B%22order%22%3A%22desc%22%7D%7D%5D%2C%22_source%22%3A%7B%7D%2C%22track_total_hits%22%3Atrue%7D\"\u003EDOAJ,\u003C\u002Fa\u003E \u003Ca href=\"https:\u002F\u002Fwww.ebsco.com\u002Fm\u002Fee\u002FMarketing\u002FtitleLists\u002Fcin-coverage.htm\" target=\"_blank\" rel=\"noopener\"\u003ECINAHL\u003C\u002Fa\u003E, \u003Ca href=\"..\u002FeditAnnouncement\u002F503\"\u003EMEDLINE\u003C\u002Fa\u003E, and the \u003Ca href=\"https:\u002F\u002Fmjl.clarivate.com\u002Fsearch-results?issn=2369-1999&hide_exact_match_fl=true&utm_source=mjl&utm_medium=share-by-link&utm_campaign=search-results-share-this-journal\"\u003EEmerging Sources Citation Index (Clarivate).\u003C\u002Fa\u003E  With a CiteScore of 4.1, JC is a Q2 journal in the field of Oncology, according to Scopus data.\u003C\u002Fp\u003E\r\n\u003Cp\u003EWe invite submissions of original research, viewpoints, reviews, tutorials, case studies, and non-conventional articles (e.g. open patient education material and software resources that are not yet evaluated but are free for others to use\u002Fimplement). \u003C\u002Fp\u003E\r\n\u003Cp\u003EIn our \"Patients' Corner,\" we invite patients and survivors to submit short essays and viewpoints on all aspects of cancer. In particular, we are interested in suggestions on improving the health care system and suggestions for new technologies, applications and approaches (this section has no article processing fees).\u003C\u002Fp\u003E\r\n\u003Cp\u003EIn case of acceptance, an Article Processing Fee will be charged to cover copyediting and typesetting costs (see \u003Ca href=\"..\u002F..\u002Fabout\u002FeditorialPolicies#custom5\"\u003Efee schedule\u003C\u002Fa\u003E).\u003C\u002Fp\u003E",googleAnalyticsId:"UA-186918-10",impactFactor:H,journalDescription:"\u003Cp\u003EPatient-centered innovations, education, and technology for cancer care, cancer survivorship, and cancer research.\u003C\u002Fp\u003E",journalInitials:"JC",footer:"\u003Cdiv\u003E\r\n\u003Cul style=\"display: flex; flex-wrap: wrap; list-style: none; justify-content: center;\"\u003E\r\n\u003Cli style=\"margin: 10px;\"\u003E\u003Ca href=\"https:\u002F\u002Fsearch.crossref.org\u002F?q=2369-1999\" target=\"_blank\" rel=\"noopener\"\u003E\u003Cimg src=\"https:\u002F\u002Fasset.jmir.pub\u002Fresources\u002Fimages\u002Fpartners\u002Fcrossref.jpg\" alt=\"Crossref Member\" \u002F\u003E\u003C\u002Fa\u003E\u003C\u002Fli\u003E\r\n\u003Cli style=\"margin: 10px;\"\u003E\u003Ca href=\"#\"\u003E\u003Cimg src=\"https:\u002F\u002Fasset.jmir.pub\u002Fresources\u002Fimages\u002Fpartners\u002Fopen-access.jpg\" alt=\"Open Access\" \u002F\u003E\u003C\u002Fa\u003E\u003C\u002Fli\u003E\r\n\u003Cli style=\"margin: 10px;\"\u003E\u003Ca href=\"http:\u002F\u002Foaspa.org\u002Fmember-record-jmir-publications-inc\u002F\" target=\"_blank\" rel=\"noopener\"\u003E\u003Cimg src=\"https:\u002F\u002Fasset.jmir.pub\u002Fresources\u002Fimages\u002Fpartners\u002Foaspa.jpg\" alt=\"Open Access Scholarly Publishers Association\" \u002F\u003E\u003C\u002Fa\u003E\u003C\u002Fli\u003E\r\n\u003Cli style=\"margin: 10px;\"\u003E\u003Ca href=\"https:\u002F\u002Fwww.trendmd.com\u002F\" target=\"_blank\" rel=\"noopener\"\u003E\u003Cimg src=\"https:\u002F\u002Fasset.jmir.pub\u002Fresources\u002Fimages\u002Fpartners\u002Ftrend-MD.jpg\" alt=\"TrendMD Member\" \u002F\u003E\u003C\u002Fa\u003E\u003C\u002Fli\u003E\r\n\u003Cli style=\"margin: 10px;\"\u003E\u003Ca href=\"https:\u002F\u002Fwww.orcid.org\u002F\" target=\"_blank\" rel=\"noopener\"\u003E\u003Cimg src=\"https:\u002F\u002Fasset.jmir.pub\u002Fresources\u002Fimages\u002Fpartners\u002FORCID.jpg\" alt=\"ORCID Member\" \u002F\u003E\u003C\u002Fa\u003E\u003C\u002Fli\u003E\r\n\u003Cli style=\"margin: 10px;\"\u003E\u003Ca href=\"https:\u002F\u002Fdoaj.org\u002Ftoc\u002F2369-1999\" target=\"_blank\" rel=\"noopener\"\u003E\u003Cimg src=\"https:\u002F\u002Fasset.jmir.pub\u002Fresources\u002Fimages\u002Fpartners\u002FDOAJ.jpg\" alt=\"Directory of Open Access Journals\" \u002F\u003E\u003C\u002Fa\u003E\u003C\u002Fli\u003E\r\n\u003Cli style=\"margin: 10px;\"\u003E\u003Ca href=\"https:\u002F\u002Fdoaj.org\u002Ftoc\u002F2369-1999\" target=\" _blank\" rel=\"noopener\"\u003E\u003Cimg src=\"https:\u002F\u002Fasset.jmir.pub\u002Fresources\u002Fimages\u002Fpartners\u002Fdoaj_seal_logo_medium.png\" alt=\"DOAJ Seal\" \u002F\u003E\u003C\u002Fa\u003E\u003C\u002Fli\u003E\r\n\u003C\u002Ful\u003E\r\n\u003C\u002Fdiv\u003E",onlineIssn:"2369-1999",searchDescription:"Patient-Centered Innovations, Education and Technology for Cancer Care and Cancer Research",searchKeywords:"Cancer, Medical, Medicine, Internet, Research, Journal, ehealth, JMIR,open access publishing, medical research",submissionChecklist:[{order:"1",content:"\u003Cp\u003EThe submission has not been previously published nor is it before another journal for consideration; or an explanation has been provided in Comments to the Editor. Related\u002Foverlapping published or submitted work will be uploaded as supplementary files so reviewers and editors can determine the degree of overlap with previous\u002Fother papers under consideration. Salami slicing of research is discouraged.\u003C\u002Fp\u003E"},{order:"2",content:"\u003Cp\u003EThe submission file is in Microsoft Word (.doc\u002F.docx) file format.\u003C\u002Fp\u003E"},{order:"3",content:"\u003Cp\u003EThe text meets this journal's formatting requirements, in particular those summarized in the \u003Ca href=\"http:\u002F\u002Fwww.jmir.org?Instructions_for_Authors:Instructions_for_Authors_of_JMIR#checklist\" target=\"_blank\"\u003EAuthor Checklist\u003C\u002Fa\u003E found in Instructions for Authors. The text employs \u003Cem\u003Eitalics\u003C\u002Fem\u003E, rather than \u003Cspan style=\"text-decoration: underline;\"\u003Eunderlining\u003C\u002Fspan\u003E or bold as emphasis; with figures and tables (portrait only, no landscape format) placed within the text, rather than at the end. Additional information has been put in separate files to be uploaded as Multimedia Appendix.\u003C\u002Fp\u003E"},{order:aH,content:"\u003Cp\u003EI have read and understood the \u003Ca href=\"..\u002F..\u002F\u002F?Instructions_for_Authors:Instructions_for_Authors_of_JMIR#Open_Access\" target=\"_blank\"\u003Efee schedule\u003C\u002Fa\u003E. In particular, I understand and agree that unless my department\u002Forganization is a \u003Ca href=\"..\u002F..\u002Fsupport.htm\" target=\"_blank\"\u003Einstitutional member\u003C\u002Fa\u003E BEFORE submission (see dropdown-list in step 1 of the submission process), I\u002Fmy department will be billed for the article processing fee (see Instructions for authors) in case of acceptance. PLEASE MENTION IN THE COVER LETTER ON SUBMISSION THAT YOU 1) AGREE TO PAY THE APF, OR 2) IF YOU THINK THAT THE APF SHOULD BE WAIVED DUE TO MEMBERSHIP OR FOR ANY OTHER REASONS. Journal sections marked with * may be eligible for a fee waiver or reduction under certain circumstances (must be justified in the comments field for the editor on submission). APFs may not apply for article categories marked with * (check instructions for authors). ** Special fees (in particular a submission fee) apply for \u003Ca href=\"..\u002F..\u002F\u002F?Instructions_for_Authors:Protocol_review\" target=\"_blank\"\u003Eresearch protocols and grant proposals\u003C\u002Fa\u003E. Note that the APF will also be billed if the author retracts the manuscript after acceptance, or if a case of scientific misconduct prevents us from publishing a manuscript after acceptance. \u003C\u002Fp\u003E\r\n\u003Cp\u003EPlease note the price increase for JMIR in July 2015.\u003C\u002Fp\u003E"},{order:"6",content:"\u003Cp\u003EAll cited webreferences (webpages, online available PDF reports) which are NOT journal articles or which do not have a DOI have been cached using WebCite (\u003Ca href=\"http:\u002F\u002Fwww.webcitation.org\" target=\"_blank\"\u003Ewww.webcitation.org\u003C\u002Fa\u003E) . Instead of citing the \"live\" webpage\u002Fwebsite, the author should cite the WebCite archived webpage. No URLs in the body of the manuscript are allowed - all URLs are cited as references.\u003C\u002Fp\u003E"},{order:"8",content:"\u003Cp\u003E(please check this checkbox even if you do not wish to fast-track as an indication that you read this). I understand that if I wish to fast-track the paper, I will pay the Fast-Track-Fee immediately after submission (a payment link will be provided after submission) or at a later stage. The FTF guarantees an editorial decision within 15 working days (see website for further instructions)\u003C\u002Fp\u003E"},{order:"9",content:"\u003Cp\u003EI understand that all author names and their affiliations for the final publication will be taken from the database (metadata form), not the submitted manuscript, thus all author names must be entered in the metadata form during submission. Authors may remove author names from the manuscript if they prefer blind review. All coauthors have been\u002Fwill be entered in the metadata form, and all coauthors fulfill ICMJE criteria in that they made 1) substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; 2) drafting the article or revising it critically for important intellectual content; and 3) final approval of the version to be published. Authors should meet conditions 1, 2, and 3.\u003C\u002Fp\u003E"},{order:"10",content:"\u003Cp\u003EP-values are reported in accordance with our \u003Ca href=\"..\u002F..\u002F\u002F?Instructions_for_Authors:Instructions_for_Authors_of_JMIR#p\" target=\"_blank\"\u003Einstructions for authors\u003C\u002Fa\u003E.\u003C\u002Fp\u003E"},{order:"11",content:"\u003Cp\u003ESince 26 Oct 2006, we now require payment of a \u003Cstrong\u003EUS$ 90 submission fee\u003C\u002Fstrong\u003E for ALL articles submitted to the \u003Cem\u003EJ Med Internet Res\u003C\u002Fem\u003E (=this journal) EXCEPT letters or invited articles (there is no submission fee for sister journals - please change the journal in the drop down list above before proceeding). You can use Paypal or a credit card immediately after submission. Authors will not be able to complete the submission process without payment. This fee cannot be waived (only exception: invited articles), needs to be paid also by institutional members, and is non-refundable. This fee is in addition to other potential fees such as the optional fast-track fee (FTF) and the article processing fee (APF) for non-members. Authors should understand that the submission fee is non-refundable, even if the manuscript is promptly rejected without peer-review (we do send out the majority of papers for peer-review, but we reserve the right to reject papers without peer-review for any reason, including the topic not being deemed interesting enough, which is a subjective decision by the editor).\u003C\u002Fp\u003E"},{order:"12",content:"\u003Cp\u003EAuthors agree that the manuscript and peer-review reports may be transferred to a JMIR sister\u002Fpartner journal (e.g. \u003Ca href=\"http:\u002F\u002Fwww.i-jmr.org\" target=\"_blank\"\u003Ei-JMR\u003C\u002Fa\u003E, \u003Ca href=\"http:\u002F\u002Fwww.researchprotocols.org\" target=\"_blank\"\u003EJMIR Res Protoc\u003C\u002Fa\u003E, JMIR mHealth, JMIR Human Factors and others), if the paper is not found suitable for publication in JMIR, but is publishable in another journal. The submission fee for that partner journal (if any) will be waived, and transfer of the peer-review reports may mean that the paper does not have to be re-reviewed. Authors will receive a notification when the manuscript is transferred, and at that time can decide if they want to pursue publication in a sister\u002Fpartner journal. If authors do NOT wish an automatic transfer to an alternative journal after rejection for JMIR, this should be noted in the cover letter.\u003C\u002Fp\u003E"}],articlesWidget:{enabled:i,count:t,label:"Recent Articles"},openReviewWidget:{enabled:i,count:t,label:"\u003Ca href=\"https:\u002F\u002Fpreprints.jmir.org\"\u003EPreprints\u003C\u002Fa\u003E Open for Peer-Review"},searchWidget:{enabled:i},partnershipsWidget:{enabled:i},submitButton:{enabled:i,label:"Submit Article"},editorInChief:"\u003Cp\u003E\u003Cspan\u003ENaomi Cahill, PhD, RD,\u003C\u002Fspan\u003E\u003Cstrong\u003E\u003Cspan\u003E \u003C\u002Fspan\u003E\u003C\u002Fstrong\u003E\u003Cspan\u003EEditor-in-Chief; Scientific Editor at JMIR Publications, Canada\u003C\u002Fspan\u003E\u003C\u002Fp\u003E"}}},journals:{data:[{journal_id:b,title:"Journal of Medical Internet Research",tag:"The leading peer-reviewed journal for digital medicine and health and health care in the internet age. June 2024 - Journal Impact Factor: 5.8. Q1 journal in \"Medical Informatics\" and \"Health Care Sciences & Services\" categories.(Source: Journal Citation Reports™ 2024 from Clarivate™)",description:a,path:aI,slug:aI,seq:b,enabled:b,environment:c,url:"https:\u002F\u002Fwww.jmir.org",batch:b,year:1999,colour:A,impact:"5.8",order:b,published:9356,transfers:a,cite_score:"14.4"},{journal_id:r,title:"JMIR Research Protocols",tag:"Ongoing trials, grant proposals, formative research, methods, early results. June 2024 - Journal Impact Factor: 1.4 (Source: Journal Citation Reports™ 2024 from Clarivate™)",description:"JMIR Res Protoc publishes research protocols, current and ongoing trials, and grant proposals in all areas of medicine (with an initial focus on ehealth\u002Fmhealth). Publish your work in this journal to let others know what you are working on, to facilitate collaboration and\u002For recruitment, to avoid duplication of efforts, to create a citable record of a research design idea, and to aid systematic reviewers in compiling evidence. Research protocols or grant proposals that are funded and have undergone peer-review will receive an expedited review if you upload peer-review reports as supplementary files.",path:"resprot",slug:"researchprotocols",seq:e,enabled:b,environment:c,url:"https:\u002F\u002Fwww.researchprotocols.org",batch:b,year:J,colour:"#837a7a",impact:"1.4",order:K,published:4452,transfers:a,cite_score:"2.4"},{journal_id:L,title:"JMIR Formative Research",tag:"Process evaluations, early results and feasibility\u002Fpilot studies of digital and non-digital interventions. June 2024 - Journal Impact Factor: 2.0 (Source: Journal Citation Reports™ 2024 from Clarivate™)",description:d,path:aJ,slug:aJ,seq:B,enabled:b,environment:c,url:"https:\u002F\u002Fformative.jmir.org",batch:e,year:M,colour:"#605959",impact:"2.0",order:N,published:3264,transfers:a,cite_score:"2.7"},{journal_id:N,title:"JMIR mHealth and uHealth",tag:"Focused on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. June 2024 - Journal Impact Factor: 5.4. Q1 journal in \"Health Care Sciences & Services\" and \"Medical Informatics\" categories. 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