The Promise of Chatbots in Clinical Trials

<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title> The Promise of Chatbots in Clinical Trials </title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <link rel="canonical" href="https://www.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/" />  <script async src="https://www.googletagmanager.com/gtag/js?id=G-YK7FCK71TD"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-YK7FCK71TD'); </script> <meta name="description" content="" /> <meta name="keywords" content="" />  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:title" content="The Promise of Chatbots in Clinical Trials"> <meta name="twitter:description" content=""> <meta name="twitter:image" content="https://www.biopharmatrend.com/files/blog/social/iStock-1486721820.jpg">  <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/" /> <meta property="og:title" content="The Promise of Chatbots in Clinical Trials" /> <meta property="og:description" content="" /> <meta property="article:published_time" content="2024-10-08T17:04:45+00:00"> <meta property="article:modified_time" content="2024-10-08T17:04:45+00:00"> <meta name="author" content="Illia Petrov"> <meta property="og:image" content="https://www.biopharmatrend.com/files/blog/social/iStock-1486721820.jpg"> <script type="application/ld+json">{ "@context": "https://schema.org", "@type": "Article", "headline": "The Promise of Chatbots in Clinical Trials", "datePublished": "2024-10-08T17:04:45+00:00", "dateModified": "2024-10-08T17:04:45+00:00", "description": "In the rapidly changing field of clinical trials, chatbots are becoming valuable tools for data analysis. These AI-driven conversational agents streamline data collection, boost patient engagement, and enhance the accuracy of data management. By automating interactions with patients, chatbots ensure consistent and precise data collection, reducing human error and operational costs. They also provide real-time insights, allowing researchers to monitor trial progress and make timely adjustments.\nDespite these benefits, challenges such as biases in AI algorithms, lack of standardization, and usability issues persist. The accuracy of chatbots is influenced by the quality of data and the sophistication of machine learning algorithms. Nevertheless, their potential to transform clinical trial data analysis is considerable, offering scalable, cost-effective solutions that improve patient experience and data reliability. Future advancements in AI and machine learning, along with rigorous validation and standardization, will be crucial for the successful integration of chatbots into clinical trial processes.\nThis blog post explores the advantages, challenges, and future prospects of using chatbots in clinical trial data analysis, providing a thorough overview of their current and potential impact on the field.", "image": "https://www.biopharmatrend.com/files/blog/social/iStock-1486721820.jpg", "url": "https://www.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://www.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/" }, "publisher": { "@type": "Organization", "name": "BiopharmaTrend", "url": "https://www.biopharmatrend.com", "logo": { "@type": "ImageObject", "url": 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class="col-lg-9 col-md-8 col-sm-12 col-xs-12"> <div class="mobile-only"> </div> <article id="post-974" class="post "> <header> <h1>The Promise of Chatbots in Clinical Trials</h1> </header> <footer class="post-meta"> <div class="tooltip"> <span class="glyphicon glyphicon-pencil"></span> by <a href="/contributor/165/">Illia Petrov</a> <a href="https://www.linkedin.com/in/embiotech/" rel="nofollow" target="_blank"> <img src="/static/img/linkedin_icon_50x50.71dcf83d1029.png" class="author-linkein-icon" /> </a>  </span>    <span style="white-space: nowrap;"> <span class="glyphicon glyphicon-folder-open"></span>   Biopharma insight </span> <div class="tooltip-text"> <div class="tooltip-close" onclick="this.parentElement.parentElement.classList.toggle('tooltip-show');"></div> <p><b>Disclaimer</b>: All opinions expressed by Contributors are their own and do not represent those of their employers, or BiopharmaTrend.com. <br> Contributors are fully responsible for assuring they own any required copyright for any content they submit to BiopharmaTrend.com. This website and its owners shall not be liable for neither information and content submitted for publication by Contributors, nor its accuracy.</p> </div> </div> <div> <span class="glyphicon glyphicon-calendar" title="Published on"></span> <time datetime="2024-10-08">Oct. 8, 2024</time>    </em> </div> <div class="tags"> <b>Topics: </b> <a href="/topic/clinical-trials/">Clinical Trials</a>    </div> <div> <strong>Share: </strong>   <a target="_blank" rel="nofollow" href="https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fwww.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/"> <img alt="Share in LinkedIn" src="/static/img/soc-in.7bc286fd757e.png" width="28" height="28"></a>  <a target="_blank" rel="nofollow" href="https://www.reddit.com/submit?url=https%3A%2F%2Fwww.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/"> <img alt="Share in Reddit" src="/static/img/soc-reddit.342e87a07115.png" width="28" height="28"></a>  <a target="_blank" rel="nofollow" href="https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/"> <img alt="Share in X" src="/static/img/soc-x.3c80e8291cba.png" width="28" height="28"></a>  <a target="_blank" rel="nofollow" href="https://news.ycombinator.com/submitlink?u=https%3A%2F%2Fwww.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/"> <img alt="Share in Hacker News" src="/static/img/soc-y.587f16a5f3f3.png" width="28" height="28"></a>  <a target="_blank" rel="nofollow" href="https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fwww.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/&display=page"> <img alt="Share in Facebook" src="/static/img/soc-fb.3031a5a810f0.png" width="28" height="28" /></a>  <a target="_blank" rel="nofollow" href="mailto:?subject=Biopharmatrend.com&body=https%3A%2F%2Fwww.biopharmatrend.com/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/"> <img alt="Send by email" src="/static/img/soc-mail.41ce3608f438.png" width="28" height="28"></a>   |  <a href="/post/974-revolutionizing-clinical-trials-the-power-and-potential-of-chatbots-in-data-analysis/pdf/" target="_blank" rel="nofollow"> <span style="vertical-align: text-top; color: deepskyblue; font-size: 1.5em;" class="glyphicon glyphicon-print"></span></a> </div> </footer> <section> <p>In the rapidly changing field of clinical trials, chatbots are becoming valuable tools for data analysis. These AI-driven conversational agents streamline data collection, boost patient engagement, and enhance the accuracy of data management. By automating interactions with patients, chatbots ensure consistent and precise data collection, reducing human error and operational costs. They also provide real-time insights, allowing researchers to monitor trial progress and make timely adjustments.</p> <p>Despite these benefits, challenges such as biases in AI algorithms, lack of standardization, and usability issues persist. The accuracy of chatbots is influenced by the quality of data and the sophistication of machine learning algorithms. Nevertheless, their potential to transform clinical trial data analysis is considerable, offering scalable, cost-effective solutions that improve patient experience and data reliability. Future advancements in AI and machine learning, along with rigorous validation and standardization, will be crucial for the successful integration of chatbots into clinical trial processes.</p> <p>This blog post explores the advantages, challenges, and future prospects of using chatbots in clinical trial data analysis, providing a thorough overview of their current and potential impact on the field.</p> <div> <hr /> <p>Clinical trials are essential for advancing medical knowledge and developing new treatments. However, they often involve complex data collection and analysis processes that can be time-consuming and prone to errors. This is where chatbots, or conversational agents, come into play. Chatbots are AI-driven tools designed to interact with users in a conversational manner, providing timely information and support.</p> <p>One of the primary benefits of chatbots in clinical trials is their ability to improve data collection and management. By interacting with patients in real-time, chatbots can gather patient-reported outcomes, adherence to treatment protocols, and other relevant data points consistently and accurately. This automation reduces the likelihood of errors associated with manual data entry and ensures that data is collected in a standardized manner.</p> <p>Chatbots also play a crucial role in enhancing patient engagement and retention. By providing timely reminders and support, they help maintain patient involvement throughout the trial period, leading to higher retention rates and more reliable data (<a href="https://pubmed.ncbi.nlm.nih.gov/32924957/">Schachner et al., 2020</a>). This is particularly important in long-term studies where maintaining participant involvement can be challenging.</p> <p>Despite these benefits, the use of chatbots in clinical trial data analysis is not without challenges. One significant issue is the introduction of biases in AI algorithms, which can stem from a lack of representative samples in the training datasets (<a href="https://pubmed.ncbi.nlm.nih.gov/32529043/">Cirillo et al., 2020</a>). These biases can lead to skewed results and affect the reliability of the chatbot's analysis.</p> <p>Another challenge is the usability of chatbots. While some studies report that chatbots are easy to learn and navigate, others indicate that users face difficulties in knowing when and how to reply, and often find the options for interaction limited (<a href="https://pubmed.ncbi.nlm.nih.gov/33439133/">Abd-Alrazaq et al., 2021</a>). This inconsistency in user experience can hinder the effective use of chatbots in clinical trial data analysis.</p> <p>The accuracy of chatbots in clinical trial data analysis is another complex issue. The quality and bias of the data, the algorithms used, and the specific applications within clinical trials all play a role in determining the accuracy of chatbots. Advancements in machine learning (ML) and natural language processing (NLP) have provided benefits in terms of accuracy, decision-making, quick processing, and handling of complex data (<a href="https://pubmed.ncbi.nlm.nih.gov/34847056/">Xu et al., 2021</a>).</p> <p>Chatbots have shown considerable potential in various aspects of clinical trial data analysis, from data collection and monitoring to behavioral data analysis and the integration of patient-reported outcomes (<a href="https://pubmed.ncbi.nlm.nih.gov/36292238/">Roy et al., 2022</a>). Their ability to streamline processes and enhance data quality makes them valuable tools in the clinical trial landscape.</p> <p>Looking ahead, the future of chatbots in clinical trial data analysis appears promising. They have the potential to improve data collection, patient monitoring, and operational efficiency. However, addressing challenges related to standardization, ethical concerns, and data security will be crucial for their successful integration into clinical trial processes (<a href="https://pubmed.ncbi.nlm.nih.gov/31008379/">Bibault et al., 2019</a><a href="https://pubmed.ncbi.nlm.nih.gov/33337337/">; Safi et al., 2020</a>).</p> <h3>Advantages of Chatbots in Clinical Trial Data Analysis</h3> <p>Chatbots offer several benefits in clinical trial data analysis, significantly improving the efficiency and effectiveness of the process.</p> <ol> <li><strong>Data Collection and Management:</strong> Chatbots can streamline the collection and management of data by interacting with patients and healthcare professionals in real-time. They automate the process of gathering patient-reported outcomes, adherence to treatment protocols, and other relevant data points, ensuring that the data is collected consistently and accurately (<a href="https://pubmed.ncbi.nlm.nih.gov/31008379/">Bibault et al., 2019</a>).</li> <li><strong>Patient Engagement and Retention:</strong> By providing timely reminders and support, chatbots help maintain patient involvement throughout the trial period. This leads to higher retention rates and more reliable data, as patients are more likely to adhere to the study protocols and report their experiences accurately (<a href="https://pubmed.ncbi.nlm.nih.gov/36841840/">Salvagno et al., 2023</a>).</li> <li><strong>Real-time Data Analysis:</strong> Chatbots can be integrated with data analysis tools to provide immediate insights into the collected data. This allows researchers to monitor the progress of the trial continuously and make necessary adjustments promptly, improving the overall quality and reliability of the trial outcomes.</li> <li><strong>Reduction of Human Error:</strong> By automating data entry and analysis tasks, chatbots reduce the likelihood of human error, a common issue in manual data handling. This ensures that the data is more accurate and reliable, leading to more valid conclusions from the trial (<a href="https://pubmed.ncbi.nlm.nih.gov/37375838/">Blanco-González et al., 2023</a>).</li> <li><strong>Scalability:</strong> Chatbots can handle large volumes of data and interact with numerous patients simultaneously, making them highly scalable. This is particularly beneficial for large-scale clinical trials that involve many participants across different locations (<a href="https://pubmed.ncbi.nlm.nih.gov/38288778/">Cascella M et al., 2024</a>).</li> <li><strong>Cost-Effectiveness:</strong> Implementing chatbots can be more cost-effective compared to traditional methods of data collection and analysis. They reduce the need for extensive human resources and minimize operational costs associated with data management.</li> <li><strong>Enhanced Patient Experience:</strong> Chatbots provide a more personalized and interactive experience for patients, improving their overall satisfaction with the clinical trial process. This can lead to better compliance and more accurate reporting of patient experiences.</li> </ol> <h3>Challenges of Using Chatbots in Clinical Trial Data Analysis</h3> <p>Despite their benefits, the use of chatbots in clinical trial data analysis presents several challenges:</p> <ol> <li><strong>Lack of Standardization:</strong> One of the primary challenges is the absence of standardized evaluation measures for chatbots. This complicates the comparability of chatbots within and between different chronic diseases, making it difficult to assess their effectiveness consistently.</li> <li><strong>Immaturity of the Field:</strong> The field of AI-based conversational agents for chronic conditions is still in its early stages, with most studies being quasi-experimental and chatbots often in the prototype stage. This indicates that the technology is not yet fully developed or validated for widespread clinical use.</li> <li><strong>Diverse and Haphazard Evaluation Approaches:</strong> The evaluation of healthcare chatbots has been varied and somewhat inconsistent, posing a barrier to the advancement of the field. This diversity in evaluation methods makes it difficult to compare the performance of different chatbots and to draw generalizable conclusions about their effectiveness.</li> <li><strong>Paucity of Objective Measures:</strong> There is a lack of objective measures in the evaluation of health chatbots, with survey designs and global usability metrics dominating. This can inhibit the advancement of the field by making it difficult to assess the true performance and impact of chatbots.</li> <li><strong>High Risk of Bias: </strong>Many studies assessing the effectiveness of chatbots have a high risk of bias, undermining the reliability of their findings. This high risk of bias, coupled with conflicting results for some outcomes, indicates that further rigorous studies are needed to draw solid conclusions about the effectiveness and safety of chatbots.</li> <li><strong>Limited Evidence and Small Cohorts:</strong> The evidence supporting the use of chatbots in clinical settings is often based on small cohort groups and low-grade evidence. This limitation has led to transformation failures in some research projects, highlighting the need for high-quality validation and evidence.</li> <li><strong>Engagement and Usability Issues:</strong> There is evidence of a decrease in engagement with chatbots over time, which can affect their long-term effectiveness in clinical trial data analysis. Inconsistencies in engagement metrics across different studies further complicate the assessment of chatbot performance.</li> </ol> <h3>Accuracy of Chatbots in Clinical Trial Data Analysis</h3> <p>The accuracy of chatbots in clinical trial data analysis is a multifaceted issue involving several considerations:</p> <ol> <li><strong>AI and Machine Learning Capabilities:</strong> Chatbots and other AI-driven tools have shown significant potential in various healthcare applications due to advancements in machine learning (ML) and natural language processing (NLP). These technologies enable chatbots to process and analyze large volumes of data quickly and accurately, which is crucial for clinical trial data analysis.</li> <li><strong>Data Quality and Bias:</strong> The accuracy of chatbots in clinical trial data analysis can be affected by the quality of the data they are trained on. Biases introduced during the data acquisition stage can lead to inconsistencies and inaccuracies in the analysis. Federated learning has been suggested as a solution in cases where there is not enough data, which can help improve the robustness of the models used by chatbots.</li> <li><strong>Evaluation and Standardization:</strong> The evaluation of chatbots in healthcare, including their use in clinical trial data analysis, has been varied and sometimes inconsistent. This lack of standardization in evaluation metrics can make it difficult to compare the performance of different chatbots and hinder the advancement of the field. More structured development and standardized evaluation processes are needed to enhance the quality and impact of chatbots in clinical trial data analysis.</li> <li><strong>Potential and Limitations:</strong> While chatbots have demonstrated potential in various healthcare applications, including the synthesis of best-practice research evidence and systematic reviews, there are still challenges to be addressed. These include issues related to inaccuracies, biases, and the need for transparency in AI-generated text (<a href="https://pubmed.ncbi.nlm.nih.gov/36513071/">Coiera & Liu, 2022</a><a href="https://pubmed.ncbi.nlm.nih.gov/37215063/">; Dave et al., 2023</a>).</li> </ol> <h3>Applications of Chatbots in Clinical Trial Data Analysis</h3> <p>Chatbots have shown significant potential in various aspects of clinical trial data analysis. One of the primary applications is in the automation and streamlining of data collection processes. For instance, chatbots can facilitate the collection of patient-reported outcome measures (PROMs), which are crucial for assessing the effectiveness of AI health technologies in clinical trials (<a href="https://pubmed.ncbi.nlm.nih.gov/36828608/">Pearce et al., 2023</a>). PROMs are particularly important in clinical areas where the assessment of health-related quality of life and symptom burden is critical (<a href="https://pubmed.ncbi.nlm.nih.gov/36828608/">Pearce et al., 2023</a>).</p> <p>Additionally, chatbots can assist in the extraction and analysis of data from electronic health records (EHRs) and other clinical and pre-clinical medical images, which are essential for regulating suitable trial methods and techniques (<a href="https://pubmed.ncbi.nlm.nih.gov/36292238/">Roy et al., 2022</a>). This capability can significantly enhance the efficiency and accuracy of data analysis in clinical trials.</p> <p>Moreover, chatbots can aid in the evaluation of patient-specific factors, such as fluid balance and kidney function recovery, to guide healthcare professionals in making informed decisions about the continuation or cessation of treatments like continuous renal replacement therapy (CRRT) (<a href="https://pubmed.ncbi.nlm.nih.gov/37887265/">Suppadungsuk S et al., 2023</a>).</p> <p>In the context of mental health, chatbots have been used to improve access to care and gather data on patient perceptions and opinions, which can be valuable for clinical trial data analysis (<a href="https://pubmed.ncbi.nlm.nih.gov/33439133/">Abd-Alrazaq et al., 2021</a>). The ability of chatbots to interact with patients using spoken, written, and visual language makes them versatile tools for data collection and analysis in clinical trials (<a href="https://pubmed.ncbi.nlm.nih.gov/32673216/">Abd-Alrazaq et al., 2020</a>).</p> <h3>Future of Chatbots in Clinical Trial Data Analysis</h3> <p>The future of chatbots in clinical trial data analysis appears promising, with several potential applications and benefits. Chatbots, also known as conversational agents, are increasingly being integrated into various aspects of healthcare, including clinical trials, due to their ability to interact with users in a conversational manner and provide timely information (<a href="https://pubmed.ncbi.nlm.nih.gov/33337337/">Safi et al., 2020</a>).</p> <p>One of the key advantages of using chatbots in clinical trials is their ability to enhance data collection and patient monitoring. Chatbots can facilitate real-time data entry and monitoring, reducing the burden on patients and researchers to manually collect and input data (<a href="https://pubmed.ncbi.nlm.nih.gov/36405414/">Shetty et al., 2022</a>). This can lead to more accurate and timely data, which is crucial for the success of clinical trials.</p> <p>Moreover, chatbots can improve patient engagement and adherence to clinical trial protocols. By providing personalized reminders and support, chatbots can help ensure that patients follow the trial procedures correctly and consistently (<a href="https://pubmed.ncbi.nlm.nih.gov/32581935/">Dekker et al., 2020</a>). This can lead to higher retention rates and more reliable data.</p> <p>The integration of artificial intelligence (AI) into chatbots further enhances their capabilities. AI-based chatbots can analyze large datasets and provide insights that may not be immediately apparent to human researchers. This can help identify patterns and trends in the data, leading to more informed decision-making and potentially accelerating the drug development process (<a href="https://pubmed.ncbi.nlm.nih.gov/32924957/">Schachner et al., 2020</a>).</p> <p>However, there are also challenges and limitations to consider. The development and implementation of chatbots in clinical trials require careful consideration of data privacy and security, as well as the need for standardized evaluation metrics to ensure their effectiveness and reliability (<a href="https://pubmed.ncbi.nlm.nih.gov/32442157/">Abd-Alrazaq et al., 2020</a>). Additionally, the current literature on AI-based chatbots is still in its early stages, with many studies being quasi-experimental and involving prototype chatbots (<a href="https://pubmed.ncbi.nlm.nih.gov/32924957/">Schachner et al., 2020</a>).</p> <p>In summary, the future of chatbots in clinical trial data analysis holds significant potential for improving data collection, patient engagement, and data analysis. However, further research and development are needed to address the challenges and ensure the successful integration of chatbots into clinical trial processes (<a href="https://pubmed.ncbi.nlm.nih.gov/33337337/">Safi et al., 2020</a>).</p> <h3>Conclusion</h3> <p>The use of chatbots in analyzing clinical trial data holds significant promise for the healthcare sector. By automating the processes of data collection and management, chatbots improve the precision and uniformity of patient-reported outcomes, thus minimizing human error and reducing operational expenses. Their capability to interact with patients in real-time leads to higher retention rates and more dependable data, which are vital for the success of clinical trials. Additionally, the scalability and cost-efficiency of chatbots make them suitable for extensive trials involving numerous participants across different locations.</p> <p>Nonetheless, issues such as the absence of standardization, high risk of bias, and limited evidence from small cohort studies underscore the necessity for more thorough research and validation. Tackling these challenges will be crucial to fully harness the benefits of chatbots in clinical trial data analysis. As AI and machine learning technologies continue to evolve, the future of chatbots in this domain appears promising, with the potential to transform data analysis and enhance patient outcomes in clinical trials.</p> </div> <h3>References</h3> <blockquote> <ol> <li>Abd-Alrazaq et al. Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review. Journal of medical Internet research (2021).</li> <li>Abd-Alrazaq et al. Technical Metrics Used to Evaluate Health Care Chatbots: Scoping Review. Journal of medical Internet research (2020).</li> <li>Abd-Alrazaq, Rababeh, Alajlani, Bewick & Househ Effectiveness and Safety of Using Chatbots to Improve Mental Health: Systematic Review and Meta-Analysis. Journal of medical Internet research (2020).</li> <li>Bibault et al. Healthcare ex Machina: Are conversational agents ready for prime time in oncology? Clinical and translational radiation oncology (2019).</li> <li>Blanco-González et al. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel, Switzerland) (2023).</li> <li>Cascella M, Laudani A, Scarpati G & Piazza O Ethical issues in pain and palliation. Current opinion in anaesthesiology (2024).</li> <li>Cirillo et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. NPJ digital medicine (2020).</li> <li>Coiera & Liu Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell reports. Medicine (2022).</li> <li>Dave, Athaluri & Singh ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Frontiers in artificial intelligence (2023).</li> <li>Dekker et al. Optimizing Students' Mental Health and Academic Performance: AI-Enhanced Life Crafting. Frontiers in psychology (2020).</li> <li>Pearce et al. The role of patient-reported outcome measures in trials of artificial intelligence health technologies: a systematic evaluation of ClinicalTrials.gov records (1997-2022). The Lancet. Digital health (2023).</li> <li>Roy, Meena & Lim Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine. Diagnostics (Basel, Switzerland) (2022).</li> <li>Safi, Abd-Alrazaq, Khalifa & Househ Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review. Journal of medical Internet research (2020).</li> <li>Salvagno, Taccone & Gerli Can artificial intelligence help for scientific writing? Critical care (London, England) (2023).</li> <li>Schachner, Keller & V Wangenheim Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review. Journal of medical Internet research (2020).</li> <li>Shetty et al. A systematic review and meta-analysis of digital application use in clinical research in pain medicine. Frontiers in digital health (2022).</li> <li>Suppadungsuk S et al. Exploring the Potential of Chatbots in Critical Care Nephrology. Medicines (Basel, Switzerland) (2023).</li> <li>Xu, Sanders, Li & Chow Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review. 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The Promise of Chatbots in Clinical Trials