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x="6" y="14" width="12" height="8"></rect></svg></a></button></div></div><div><div class="flex flex-wrap"><p class=" text-primary font-semibold">Commentary</p><div class="h-[16px] border-l-2 border-gray-400 mt-1 mx-1"></div><p class=" text-primary font-semibold">Article</p><div class="h-[16px] border-l-2 border-gray-400 mt-1 mx-1 "></div><time class="text-gray-500 " dateTime="2024-10-15T16:00:32.399">October 15, 2024</time></div><h1 class="text-[26px] font-medium leading-8">Revolutionizing Pharma: The Power of AI and Chatbots in Clinical Trials and Beyond</h1><div class="py-3 text-gray-600 md:flex flex-col md:justify-between"><div class="flex flex-col xs:flex-row"><p class="mr-1 self-start">Author(s):</p><div class="flex flex-col xs:flex-row mb-3 md:mb-0"><div class="flex flex-wrap"><span class="text-md mr-2"><a class="text-author text-gray-500 hover:text-primary underline hover:no-underline decoration-gray-400" href="/authors/steven-e-hyler-md">Steven E. Hyler, MD</a></span></div></div></div><div class="max-w-full"><div class="flex flex-wrap sm:flex-nowrap items-center w-fit my-2"></div><div class="w-full flex flex-col sm:flex-row justify-between mt-2"><div class="block md:hidden "><div class="mt-2 flex items-center max-w-fit"><button title="Revolutionizing Pharma: The Power of AI and Chatbots in Clinical Trials and Beyond" aria-label="facebook" class="react-share__ShareButton" style="background-color:transparent;border:none;padding:0;font:inherit;color:inherit;cursor:pointer"><svg viewBox="0 0 64 64" width="32" height="32"><circle cx="32" cy="32" r="31" fill="#3b5998"></circle><path d="M34.1,47V33.3h4.6l0.7-5.3h-5.3v-3.4c0-1.5,0.4-2.6,2.6-2.6l2.8,0v-4.8c-0.5-0.1-2.2-0.2-4.1-0.2 c-4.1,0-6.9,2.5-6.9,7V28H24v5.3h4.6V47H34.1z" fill="white"></path></svg></button><button aria-label="twitter" class="react-share__ShareButton" style="background-color:transparent;border:none;padding:0;font:inherit;color:inherit;cursor:pointer"><svg fill="#DC7633" xmlns="http://www.w3.org/2000/svg" width="32" zoomAndPan="magnify" viewBox="0 0 375 374.9999" height="32" preserveAspectRatio="xMidYMid meet" version="1.0"><defs><path d="M 7.09375 7.09375 L 367.84375 7.09375 L 367.84375 367.84375 L 7.09375 367.84375 Z M 7.09375 7.09375 " fill="#000000"></path></defs><g><path d="M 187.46875 7.09375 C 87.851562 7.09375 7.09375 87.851562 7.09375 187.46875 C 7.09375 287.085938 87.851562 367.84375 187.46875 367.84375 C 287.085938 367.84375 367.84375 287.085938 367.84375 187.46875 C 367.84375 87.851562 287.085938 7.09375 187.46875 7.09375 " fill-opacity="1" fill-rule="nonzero" fill="#000000"></path></g><g transform="translate(85, 75)"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" version="1.1" height="215" width="215"><path d="M18.244 2.25h3.308l-7.227 8.26 8.502 11.24H16.17l-5.214-6.817L4.99 21.75H1.68l7.73-8.835L1.254 2.25H8.08l4.713 6.231zm-1.161 17.52h1.833L7.084 4.126H5.117z" fill="#ffffff"></path></svg> </g></svg></button><button aria-label="linkedin" class="react-share__ShareButton" style="background-color:transparent;border:none;padding:0;font:inherit;color:inherit;cursor:pointer"><svg viewBox="0 0 64 64" width="32" height="32"><circle cx="32" cy="32" r="31" fill="#007fb1"></circle><path d="M20.4,44h5.4V26.6h-5.4V44z M23.1,18c-1.7,0-3.1,1.4-3.1,3.1c0,1.7,1.4,3.1,3.1,3.1 c1.7,0,3.1-1.4,3.1-3.1C26.2,19.4,24.8,18,23.1,18z M39.5,26.2c-2.6,0-4.4,1.4-5.1,2.8h-0.1v-2.4h-5.2V44h5.4v-8.6 c0-2.3,0.4-4.5,3.2-4.5c2.8,0,2.8,2.6,2.8,4.6V44H46v-9.5C46,29.8,45,26.2,39.5,26.2z" fill="white"></path></svg></button><button title="Revolutionizing Pharma: The Power of AI and Chatbots in Clinical Trials and Beyond" aria-label="pinterest" class="react-share__ShareButton" style="background-color:transparent;border:none;padding:0;font:inherit;color:inherit;cursor:pointer"><svg viewBox="0 0 64 64" width="32" height="32"><circle cx="32" cy="32" r="31" fill="#cb2128"></circle><path d="M32,16c-8.8,0-16,7.2-16,16c0,6.6,3.9,12.2,9.6,14.7c0-1.1,0-2.5,0.3-3.7 c0.3-1.3,2.1-8.7,2.1-8.7s-0.5-1-0.5-2.5c0-2.4,1.4-4.1,3.1-4.1c1.5,0,2.2,1.1,2.2,2.4c0,1.5-0.9,3.7-1.4,5.7 c-0.4,1.7,0.9,3.1,2.5,3.1c3,0,5.1-3.9,5.1-8.5c0-3.5-2.4-6.1-6.7-6.1c-4.9,0-7.9,3.6-7.9,7.7c0,1.4,0.4,2.4,1.1,3.1 c0.3,0.3,0.3,0.5,0.2,0.9c-0.1,0.3-0.3,1-0.3,1.3c-0.1,0.4-0.4,0.6-0.8,0.4c-2.2-0.9-3.3-3.4-3.3-6.1c0-4.5,3.8-10,11.4-10 c6.1,0,10.1,4.4,10.1,9.2c0,6.3-3.5,11-8.6,11c-1.7,0-3.4-0.9-3.9-2c0,0-0.9,3.7-1.1,4.4c-0.3,1.2-1,2.5-1.6,3.4 c1.4,0.4,3,0.7,4.5,0.7c8.8,0,16-7.2,16-16C48,23.2,40.8,16,32,16z" fill="white"></path></svg></button><button aria-label="email" class="react-share__ShareButton" style="background-color:transparent;border:none;padding:0;font:inherit;color:inherit;cursor:pointer"><svg viewBox="0 0 64 64" width="32" height="32"><circle cx="32" cy="32" r="31" fill="#7f7f7f"></circle><path d="M17,22v20h30V22H17z M41.1,25L32,32.1L22.9,25H41.1z M20,39V26.6l12,9.3l12-9.3V39H20z" fill="white"></path></svg></button><a class="print-wrap flex justify-center items-center cursor-pointer"><svg id="print" xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="currentColor" class="print bi bi-printer" viewBox="0 0 16 16"> <path d="M2.5 8a.5.5 0 1 0 0-1 .5.5 0 0 0 0 1z"></path> <path d="M5 1a2 2 0 0 0-2 2v2H2a2 2 0 0 0-2 2v3a2 2 0 0 0 2 2h1v1a2 2 0 0 0 2 2h6a2 2 0 0 0 2-2v-1h1a2 2 0 0 0 2-2V7a2 2 0 0 0-2-2h-1V3a2 2 0 0 0-2-2H5zM4 3a1 1 0 0 1 1-1h6a1 1 0 0 1 1 1v2H4V3zm1 5a2 2 0 0 0-2 2v1H2a1 1 0 0 1-1-1V7a1 1 0 0 1 1-1h12a1 1 0 0 1 1 1v3a1 1 0 0 1-1 1h-1v-1a2 2 0 0 0-2-2H5zm7 2v3a1 1 0 0 1-1 1H5a1 1 0 0 1-1-1v-3a1 1 0 0 1 1-1h6a1 1 0 0 1 1 1z"></path></svg></a></div><style> .print-wrap { width: 32px; height: 32px; background: #7F7F7F; border-radius: 100%; } .print { background: #7F7F7F; color: white; padding: 2px; border-radius: 100%; } </style></div><style> video::cue { display: inline; background-color: #b8dcf6; padding: 2px 2px; } audio { height: 40px; } .rhap_container { width: 300px !important; border-radius: 100px !important; height: 40px !important; box-shadow: 0px 0px 2px 2px rgba(0,0,0,0.1); } .rhap_progress-section { width: 150px; margin-left: 35px; } .rhap_controls-section { position: relative; bottom: .75rem; } .rhap_time { font-size: 12px; color: rgb(0,55,103); } .rhap_progress-bar { color: rgb(0,55,103) !important; } .rhap_progress-filled { background-color: rgb(0,55,103) !important; } .rhap_progress-indicator { height: 15px; width: 5px; top: -5px; margin-left: 1px; background-color: rgb(0,55,103) !important; } .rhap_repeat-button { display: none; } .rhap_volume-bar, rhap_volume-button, .rhap_volume-indicator { background: rgb(0,55,103) !important; } .rhap_volume-bar { height: 2px; width: 35px; position: relative; left: 22px; bottom: 21px; } .rhap_volume-button { // width: 5px; // height: 5px; flex: 0 0 26px; position: relative; left: 22px; bottom: 21px; } .rhap_volume-button svg { height: 18px; width: 18px; } .rhap_volume-indicator { height: 8px; width: 8px; top: -2.75px } .rhap_button-clear { color: rgb(0,55,103) !important; } .rhap_play-pause-button { color: rgb(0,55,103) !important; font-size: 30px !important; width: 30px !important; height: 30px !important; position: relative; right: 90px; bottom: 22px; } .rhap_main-controls button { color: rgb(0,55,103) !important; } audio::-webkit-media-controls-play-button, video::-webkit-media-controls-play-button { -webkit-appearance: media-play-button; color: #b8dcf6; } audio::-webkit-media-controls-panel { background-color: white !important; color: #000; } audio::-webkit-media-controls-current-time-display, audio::-webkit-media-controls-time-remaining-display { font-size: 12px; } </style></div></div></div><div class=" lg:w-full flex flex-col lg:flex-row lg:items-center lg:justify-end"></div><p class="py-2 mb-2 text-sm italic text-gray-600">Learn more about how AI can transform mental health care. </p><div class="py-2"><div class="blockText_blockContent__TbCXh"><div class=""><div style="width:50%;float:left;max-width:525px;margin:0 1.5rem 1.5rem 0;clear:both;cursor:" class=" figure"><div class="flex-none relative text-center"><span style="box-sizing:border-box;display:inline-block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:relative;max-width:100%"><span style="box-sizing:border-box;display:block;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;max-width:100%"><img style="display:block;max-width:100%;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0" alt="" aria-hidden="true" src="data:image/svg+xml,%3csvg%20xmlns=%27http://www.w3.org/2000/svg%27%20version=%271.1%27%20width=%274630%27%20height=%272595%27/%3e"/></span><img alt="AI" title="AI" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" decoding="async" data-nimg="intrinsic" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:contain"/><noscript><img alt="AI" title="AI" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F0vv8moc6%2Fpsychtimes%2F789b7f087e7a75b347f050662da91640963b12bd-4630x2595.jpg%3Ffit%3Dcrop%26auto%3Dformat&w=3840&q=75 1x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F0vv8moc6%2Fpsychtimes%2F789b7f087e7a75b347f050662da91640963b12bd-4630x2595.jpg%3Ffit%3Dcrop%26auto%3Dformat&w=3840&q=75" decoding="async" data-nimg="intrinsic" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:contain" loading="lazy"/></noscript></span></div><div id="image-caption" class="text-gray-500 italic"><div class="blockText_blockContent__TbCXh"><p class="pb-2">Senuka Nuwansith/AdobeStock</p></div></div><div class="top-[-100%] block w-[1px] transition-opacity duration-500 ease-in-out opacity-0 overflow-hidden"><img class="m-auto absolute inset-0 max-w-[0%] max-h-[0%] border-[3px] border-solid border-white shadow-[0px_0px_8px_rgba(0,0,0,0.3)] box-border transition ease-in-out duration-500" src="https://cdn.sanity.io/images/0vv8moc6/psychtimes/789b7f087e7a75b347f050662da91640963b12bd-4630x2595.jpg?fit=crop&auto=format"/></div></div><style> #image-caption p{ font-size: 12px; max-width: 525px; margin: 0 auto; text-align: center; } </style></div><p class="pb-2"><strong>CHATBOT CORNER</strong></p><p class="pb-2">The pharmaceutical industry is in the midst of a transformative revolution driven by advancements in large language models (LLMs), generative artificial intelligence (AI), and chatbots. These technologies offer unprecedented opportunities to enhance clinical trials, streamline the dissemination of pharmaceutical information, and accelerate drug discovery.<sup class="text-inherit">1</sup> In this manuscript, we explore how AI is being utilized to improve efficiency, ensure better patient outcomes, and revolutionize communication strategies in the pharmaceutical landscape. Furthermore, we delve into the current challenges and opportunities of AI integration and highlight significant AI investments in the industry.</p><p class="pb-2"></p><p class="pb-2"><strong>Enhancing Clinical Trials with Generative AI</strong></p><p class="pb-2">Clinical trials are the backbone of pharmaceutical research, providing the evidence needed to confirm the safety and efficacy of new medications. Traditionally, these trials are complex, time-consuming, and costly.<sup class="text-inherit">1</sup> Generative AI and LLMs are poised to revolutionize this process in several key ways:</p><p class="pb-2"><strong><em>Participant recruitment and retention.</em></strong> Recruiting and retaining participants is one of the primary challenges in clinical trials. Generative AI can analyze vast datasets to identify potential participants who meet trial criteria, considering factors such as medical history, genetic data, and social determinants of health. AI-powered chatbots can maintain engagement with participants through personalized communication, reminders, and support, thereby improving retention rates.</p><p class="pb-2"><strong><em>Data analysis and interpretation.</em></strong> The volume of data generated during clinical trials can be overwhelming. LLMs can process and analyze this data more efficiently than traditional methods, identifying patterns and correlations that might be missed by human analysts.1 This capability accelerates the decision-making process, allowing researchers to make informed decisions more quickly.</p><p class="pb-2"><strong><em>Real-time monitoring and reporting.</em></strong> AI-driven tools can monitor clinical trial data in real-time, providing immediate insights and alerts for any anomalies or adverse events. This real-time capability ensures that issues are promptly addressed, enhancing patient safety and the reliability of trial outcomes.<sup class="text-inherit">2</sup></p><p class="pb-2"><strong><em>Automated documentation and compliance.</em></strong> Generative AI can automate the creation and management of trial documentation, ensuring that all regulatory requirements are met. This automation reduces the administrative burden on researchers and minimizes the risk of human error.</p><p class="pb-2"></p><p class="pb-2">Recent studies indicate that using AI in clinical trials could lead to cost savings of up to 70% per trial and a reduction in timelines by 80%.<sup class="text-inherit">3</sup> This transformation is significant, considering the traditional time frame of 5 to 6 years for drug discovery, which AI technology is now shortening to just 1 year.</p><p class="pb-2"></p><p class="pb-2"><strong>Streamlining Communication with Health Care Professionals and Patients</strong></p><p class="pb-2">Effective communication is crucial for the successful launch and ongoing use of pharmaceutical products. Generative AI and chatbots significantly enhance how pharmaceutical companies interact with health care professionals and patients:</p><p class="pb-2"><strong><em>Personalized medical information.</em></strong> LLMs can generate tailored medical information for health care providers, ensuring they receive the most relevant and up-to-date data about new medications. This personalized approach helps health care professionals make informed decisions when prescribing new treatments.<sup class="text-inherit">4</sup></p><p class="pb-2"><strong><em>Patient education and support.</em></strong> Chatbots powered by generative AI can provide patients with personalized education about their medications, including dosage instructions, potential adverse effects, and lifestyle recommendations. These chatbots can also offer emotional support and answer questions in real-time, improving patient adherence to treatment regimens.<sup class="text-inherit">5</sup></p><p class="pb-2"><strong><em>Interactive product launches.</em></strong> When launching new products, pharmaceutical companies can use AI-driven chatbots to create interactive and engaging experiences for health care professionals and patients. These chatbots can provide detailed information about the new product, answer questions, and collect feedback, making the launch more dynamic and effective.</p><p class="pb-2"><strong><em>Monitoring and feedback.</em></strong> Post-launch, AI tools can continuously monitor patient feedback and real-world data to identify any emerging issues or trends.<sup class="text-inherit">6</sup> This ongoing monitoring allows companies to respond quickly to any concerns, ensuring that their products remain safe and effective.</p><p class="pb-2"></p><p class="pb-2">Companies like Sanofi are already harnessing AI to engage with underrepresented communities in clinical trials, utilizing AI tools to facilitate communication and increase participation.<sup class="text-inherit">7</sup> A recent survey revealed that 80% of pharmaceutical and life sciences professionals are currently using AI for drug discovery, highlighting the growing adoption of these technologies in the industry.<sup class="text-inherit">3</sup></p><p class="pb-2"></p><p class="pb-2"><strong>Impact on Drug Discovery</strong></p><p class="pb-2">AI's most significant impact in the pharmaceutical industry is in drug discovery. AI accelerates the identification of potential drug candidates and optimizes molecular design by analyzing biological data, predicting drug efficacy and safety profiles, and shortening the time from laboratory to market.<sup class="text-inherit">4</sup></p><p class="pb-2"></p><p class="pb-2">The first stage of drug discovery typically takes up to 6 years, with only 1 out of 10 medical products entering clinical trials and gaining US Food and Drug Administration approval.<sup class="text-inherit">1</sup> AI revolutionizes this process through virtual screening, molecular modeling, and predictive analytics, enabling more efficient and effective medications.</p><p class="pb-2"></p><p class="pb-2">Pharmaceutical companies like Alexion AstraZeneca are leveraging AI to identify drug targets for rare neurodegenerative and neuromuscular diseases. Their work demonstrates AI's capability to comb through genomic datasets derived from human tissue, accelerating the drug discovery process and enhancing precision.<sup class="text-inherit">7</sup></p><p class="pb-2"></p><p class="pb-2">Furthermore, the development of AI model tools such as DeepChem, RDKit, ChemBERTa, and others has enabled virtual screening, molecular modeling, and predictive analytics in drug discovery. These models can analyze vast amounts of chemical and biological data to identify potential drug candidates, optimize lead compounds, and predict their properties. AI's capabilities in this domain are becoming indispensable, with some studies indicating that AI can reduce the time to develop a drug by 4 years, saving the industry up to $26 billion.<sup class="text-inherit">3</sup></p><p class="pb-2"></p><p class="pb-2"><strong>Operations and Supply Chain Management</strong></p><p class="pb-2">AI's predictive analytics model and automation capabilities play a crucial role in supply chain management. Supply chain optimization with AI integration enables accurate demand forecasting, efficient inventory management, and optimized production schedules. These enhancements lead to more efficient distribution networks, reduced waste, lower costs, and a more resilient supply chain.<sup class="text-inherit">2</sup></p><p class="pb-2"></p><p class="pb-2">AI's integration into the pharmaceutical supply chain results in end-to-end process visibility, enabling the identification of process inefficiencies and generating actionable insights in real-time. AI can optimize inventory management, keep stock levels as low as possible, and improve decision-making based on warehousing and demand. AI's predictive capabilities also aid in predictive maintenance, generating strategic insights from IoT sensors and improving operational accuracy, repeatability, and throughput.</p><p class="pb-2"></p><p class="pb-2">The economic value AI applications could bring to the pharmaceutical industry is estimated to be between $350 billion and $410 billion annually by 2025.<sup class="text-inherit">6</sup> This integration not only ensures the timely delivery of medications but also maintains the integrity of sensitive products, contributing to patient safety and health care efficiency.</p><p class="pb-2"></p><p class="pb-2"><strong>Noteworthy Pharma AI Investments in 2023</strong></p><p class="pb-2">Several major pharmaceutical companies have made significant investments in AI, illustrating the industry's commitment to leveraging AI's emerging capabilities:</p><p class="pb-2"><strong><em>Sanofi</em>.</strong> The French pharma giant rolled out its AI app, plai, which aggregates the company's internal data across all activities and functions. Sanofi uses AI to accelerate mRNA research and identify clinical trial sites for more diverse participation. Sanofi's CEO, Paul Hudson, emphasized their goal to become "the first pharma company powered by artificial intelligence at scale."<sup class="text-inherit">7</sup></p><p class="pb-2"><strong><em>Alexion AstraZeneca Rare Disease.</em></strong> In collaboration with Verge Genomics, Alexion AstraZeneca is leveraging machine learning to identify drug targets for rare neurodegenerative and neuromuscular diseases. This 4-year, multi-target deal exemplifies AI's potential in target identification and the acceleration of drug discovery for complex conditions.<sup class="text-inherit">7</sup></p><p class="pb-2"><strong><em>Boehringer Ingelheim.</em></strong> Partnering with Phenomic AI, Boehringer Ingelheim is using AI and machine learning to discover stroma-rich cancer targets. Their use of AI to analyze single-cell RNA datasets for novel therapeutic targets showcases the potential of AI in oncology research.<sup class="text-inherit">7</sup></p><p class="pb-2"><strong><em>Pfizer.</em></strong> As an early adopter of Google Cloud's Target and Lead Identification Suite, Pfizer uses AI-powered tools to streamline the drug discovery process. This partnership highlights how tech giants and pharmaceutical companies collaborate to harness AI's power in drug development.<sup class="text-inherit">7</sup></p><p class="pb-2"><strong><em>Novartis.</em></strong> As an investor in generative AI company Yseop, Novartis aims to automate the entire documentation process from preclinical trials through FDA approval. In 2023 alone, Yseop generated over 10,000 reports, demonstrating how AI can eliminate thousands of hours of writing and review time.<sup class="text-inherit">7</sup></p><p class="pb-2"></p><p class="pb-2">These investments underscore the increasing recognition of AI's value in the pharmaceutical industry, from drug discovery to clinical trial management and beyond.</p><p class="pb-2"></p><p class="pb-2"><strong>Ethical Considerations and Challenges</strong></p><p class="pb-2">While the potential benefits of using generative AI and chatbots in the pharmaceutical industry are immense, it is crucial to address the ethical considerations and challenges associated with these technologies:</p><p class="pb-2"><strong><em>Data privacy and security.</em></strong> The use of AI in clinical trials and patient communication requires access to sensitive medical data. Ensuring the privacy and security of this data is of utmost importance. Pharmaceutical companies must implement robust data protection measures and comply with regulations such as GDPR and HIPAA.<sup class="text-inherit">3</sup></p><p class="pb-2"><strong><em>Bias and fairness.</em></strong> AI algorithms can inadvertently perpetuate biases present in the training data. It is essential to develop and deploy AI systems that are fair and unbiased, ensuring that all patient populations are treated equitably.<sup class="text-inherit">5</sup> This issue is particularly critical given the diverse populations affected by different diseases.</p><p class="pb-2"><strong><em>Transparency and accountability.</em></strong> The decisions made by AI systems can significantly impact patient care and trial outcomes. It is crucial to maintain transparency in how these systems operate and ensure accountability for their decisions. Clear guidelines and oversight mechanisms should be established to monitor AI-driven processes.<sup class="text-inherit">2</sup></p><p class="pb-2"><strong><em>Integration with existing systems.</em></strong> Integrating AI tools with existing clinical trial and health care systems can be challenging. Pharmaceutical companies must invest in training and infrastructure to ensure seamless integration and maximize the benefits of these technologies.<sup class="text-inherit">6</sup></p><p class="pb-2"><strong><em>Lack of transparency and interpretability.</em></strong> AI models, especially deep learning models, are often seen as "black boxes" due to their complexity. This lack of transparency can make it challenging to interpret the results and ensure that AI systems make decisions based on sound reasoning. This is particularly important in the pharmaceutical industry, where decisions can have life-altering consequences.<sup class="text-inherit">3</sup></p><p class="pb-2"><strong><em>Limited ability to account for variability.</em></strong> AI models sometimes struggle to account for the complexity and variability of biological systems. Pharmaceutical companies must combine AI-based models with traditional experimental methods to ensure the safety and efficacy of drugs.<sup class="text-inherit">4</sup></p><p class="pb-2"><strong><em>Regulatory and ethical compliance.</em></strong> AI applications must comply with existing regulatory standards, including those for clinical trials and drug approval. The ethical implications of using AI in health care, such as informed consent and the potential for unintended consequences, must be carefully considered.<sup class="text-inherit">5</sup></p><p class="pb-2"></p><p class="pb-2"><strong>Concluding Thoughts</strong></p><p class="pb-2">The integration of LLMs, generative AI, and chatbots into the pharmaceutical industry represents a significant leap forward in how clinical trials are conducted and how information about medications is communicated. As these technologies continue to evolve, their impact will only grow, leading to more efficient research processes, improved patient outcomes, and more effective communication strategies.</p><p class="pb-2"></p><p class="pb-2">Pharmaceutical companies that embrace these innovations will be better positioned to navigate the complexities of clinical trials and product launches in the modern era. By leveraging AI's capabilities, these companies can ensure that their research is not only cutting-edge but also patient-centric and responsive to the needs of health care professionals and patients alike.</p><p class="pb-2"></p><p class="pb-2">However, this transformation comes with challenges that must be addressed. Ethical considerations, data privacy, transparency, and integration with existing systems are all crucial factors that must be managed to harness AI's full potential responsibly. By doing so, pharmaceutical companies can usher in a new era of efficiency, effectiveness, and patient engagement, revolutionizing health care as we know it.</p><p class="pb-2"></p><p class="pb-2">As we look to the future, it is imperative for the pharmaceutical industry to continue exploring the applications of AI while remaining vigilant about its ethical implications. The potential of AI to revolutionize drug development, improve patient outcomes, and lead to a more efficient and innovative health care system is immense. By addressing the challenges and leveraging AI’s capabilities, the pharmaceutical industry stands on the brink of a new era of discovery and advancement.</p><p class="pb-2"></p><p class="pb-2"><strong>Dr Hyler </strong><em>is professor emeritus of psychiatry at Columbia University Medical Center.</em></p><p class="pb-2"></p><p class="pb-2"><strong>References</strong></p><p class="pb-2">1. Woo M. <a rel="nofollow noreferrer noopener" target="_blank" href="https://pubmed.ncbi.nlm.nih.gov/31554996/">An AI boost for clinical trials.</a> <em>Nature</em>. 2019;573(7775):S100-S102.</p><p class="pb-2">2. Gholap AD, Uddin MJ, Faiyazuddin M, et al. <a rel="nofollow noreferrer noopener" target="_blank" href="https://pubmed.ncbi.nlm.nih.gov/38878397/">Advances in artificial intelligence for drug delivery and development: a comprehensive review.</a> <em>Comput Biol Med</em>. 2024;178:108702.</p><p class="pb-2">3. Varol D. AI in the pharmaceutical industry: innovations and challenges. <em>Scilife</em>. January 30, 2024. Accessed October 14, 2024. <a rel="nofollow noreferrer noopener" target="_blank" href="https://www.scilife.io/blog/ai-pharma-innovation-challenges">https://www.scilife.io/blog/ai-pharma-innovation-challenges</a></p><p class="pb-2">4. Srivastava V, Parveen B, Parveen R. <a rel="nofollow noreferrer noopener" target="_blank" href="https://pubmed.ncbi.nlm.nih.gov/37779408/">Artificial intelligence in drug formulation and development: applications and future prospects.</a> <em>Curr Drug Metab</em>. 2023;24(9):622-634.</p><p class="pb-2">5. Singh S, Kumar R, Payra S, Singh SK. <a rel="nofollow noreferrer noopener" target="_blank" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539991/">Artificial intelligence and machine learning in pharmacological research: bridging the gap between data and drug discovery.</a> <em>Cureus</em>. 2023;15(8):e44359.</p><p class="pb-2">6. Shah B, Viswa CA, Zurkiya D, et al. Generative AI in the pharmaceutical industry: moving from hype to reality. January 9, 2024. Accessed October 14, 2024. <a rel="nofollow noreferrer noopener" target="_blank" href="https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality">https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality</a></p><p class="pb-2">7. Pecci A. 5 noteworthy pharma AI investments in 2023. <em>Pharmavoice</em>. December 19, 2023. Accessed October 14, 2024. <a rel="nofollow noreferrer noopener" target="_blank" href="https://www.pharmavoice.com/news/pharma-ai-investment-pfizer-sanofi-novartis/702882/">https://www.pharmavoice.com/news/pharma-ai-investment-pfizer-sanofi-novartis/702882/</a></p><p class="pb-2"></p></div></div><div class="flex items-center lg:w-3/4 mb-4 pb-12"></div><div class="jsx-19ede9f0a5a45918 py-4 relative bg-primary md:px-8 -ml-6 xs:ml-0 w-screen xs:w-auto"><div class="jsx-19ede9f0a5a45918 px-4 sm:px-0"><div class="flex justify-between items-center py-1 space-x-4 border-0 select-none sm:border-b border-secondary"><div class="text-3xl text-white text-lg sm:text-3xl">Related Videos</div></div></div><div style="scroll-snap-type:none" class="jsx-19ede9f0a5a45918 flex items-start overflow-x-auto space-x-4 py-4 relative mx-auto w-full pl-4"><a id="" class="w-[200px] h-fit space-y-3 flex-none select-none no-underline" style="scroll-snap-align:center;text-decoration:none" 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These technologies offer unprecedented opportunities to enhance clinical trials, streamline the dissemination of pharmaceutical information, and accelerate drug discovery.1 In this manuscript, we explore how AI is being utilized to improve efficiency, ensure better patient outcomes, and revolutionize communication strategies in the pharmaceutical landscape. Furthermore, we delve into the current challenges and opportunities of AI integration and highlight significant AI investments in the industry.\n\n\n\nEnhancing Clinical Trials with Generative AI\n\nClinical trials are the backbone of pharmaceutical research, providing the evidence needed to confirm the safety and efficacy of new medications. Traditionally, these trials are complex, time-consuming, and costly.1 Generative AI and LLMs are poised to revolutionize this process in several key ways:\n\nParticipant recruitment and retention. Recruiting and retaining participants is one of the primary challenges in clinical trials. Generative AI can analyze vast datasets to identify potential participants who meet trial criteria, considering factors such as medical history, genetic data, and social determinants of health. AI-powered chatbots can maintain engagement with participants through personalized communication, reminders, and support, thereby improving retention rates.\n\nData analysis and interpretation. The volume of data generated during clinical trials can be overwhelming. LLMs can process and analyze this data more efficiently than traditional methods, identifying patterns and correlations that might be missed by human analysts.1 This capability accelerates the decision-making process, allowing researchers to make informed decisions more quickly.\n\nReal-time monitoring and reporting. AI-driven tools can monitor clinical trial data in real-time, providing immediate insights and alerts for any anomalies or adverse events. This real-time capability ensures that issues are promptly addressed, enhancing patient safety and the reliability of trial outcomes.2\n\nAutomated documentation and compliance. Generative AI can automate the creation and management of trial documentation, ensuring that all regulatory requirements are met. This automation reduces the administrative burden on researchers and minimizes the risk of human error.\n\n\n\nRecent studies indicate that using AI in clinical trials could lead to cost savings of up to 70% per trial and a reduction in timelines by 80%.3 This transformation is significant, considering the traditional time frame of 5 to 6 years for drug discovery, which AI technology is now shortening to just 1 year.\n\n\n\nStreamlining Communication with Health Care Professionals and Patients\n\nEffective communication is crucial for the successful launch and ongoing use of pharmaceutical products. Generative AI and chatbots significantly enhance how pharmaceutical companies interact with health care professionals and patients:\n\nPersonalized medical information. LLMs can generate tailored medical information for health care providers, ensuring they receive the most relevant and up-to-date data about new medications. This personalized approach helps health care professionals make informed decisions when prescribing new treatments.4\n\nPatient education and support. Chatbots powered by generative AI can provide patients with personalized education about their medications, including dosage instructions, potential adverse effects, and lifestyle recommendations. These chatbots can also offer emotional support and answer questions in real-time, improving patient adherence to treatment regimens.5\n\nInteractive product launches. When launching new products, pharmaceutical companies can use AI-driven chatbots to create interactive and engaging experiences for health care professionals and patients. These chatbots can provide detailed information about the new product, answer questions, and collect feedback, making the launch more dynamic and effective.\n\nMonitoring and feedback. Post-launch, AI tools can continuously monitor patient feedback and real-world data to identify any emerging issues or trends.6 This ongoing monitoring allows companies to respond quickly to any concerns, ensuring that their products remain safe and effective.\n\n\n\nCompanies like Sanofi are already harnessing AI to engage with underrepresented communities in clinical trials, utilizing AI tools to facilitate communication and increase participation.7 A recent survey revealed that 80% of pharmaceutical and life sciences professionals are currently using AI for drug discovery, highlighting the growing adoption of these technologies in the industry.3\n\n\n\nImpact on Drug Discovery\n\nAI's most significant impact in the pharmaceutical industry is in drug discovery. AI accelerates the identification of potential drug candidates and optimizes molecular design by analyzing biological data, predicting drug efficacy and safety profiles, and shortening the time from laboratory to market.4\n\n\n\nThe first stage of drug discovery typically takes up to 6 years, with only 1 out of 10 medical products entering clinical trials and gaining US Food and Drug Administration approval.1 AI revolutionizes this process through virtual screening, molecular modeling, and predictive analytics, enabling more efficient and effective medications.\n\n\n\nPharmaceutical companies like Alexion AstraZeneca are leveraging AI to identify drug targets for rare neurodegenerative and neuromuscular diseases. Their work demonstrates AI's capability to comb through genomic datasets derived from human tissue, accelerating the drug discovery process and enhancing precision.7\n\n\n\nFurthermore, the development of AI model tools such as DeepChem, RDKit, ChemBERTa, and others has enabled virtual screening, molecular modeling, and predictive analytics in drug discovery. These models can analyze vast amounts of chemical and biological data to identify potential drug candidates, optimize lead compounds, and predict their properties. AI's capabilities in this domain are becoming indispensable, with some studies indicating that AI can reduce the time to develop a drug by 4 years, saving the industry up to $26 billion.3\n\n\n\nOperations and Supply Chain Management\n\nAI's predictive analytics model and automation capabilities play a crucial role in supply chain management. Supply chain optimization with AI integration enables accurate demand forecasting, efficient inventory management, and optimized production schedules. These enhancements lead to more efficient distribution networks, reduced waste, lower costs, and a more resilient supply chain.2\n\n\n\nAI's integration into the pharmaceutical supply chain results in end-to-end process visibility, enabling the identification of process inefficiencies and generating actionable insights in real-time. AI can optimize inventory management, keep stock levels as low as possible, and improve decision-making based on warehousing and demand. AI's predictive capabilities also aid in predictive maintenance, generating strategic insights from IoT sensors and improving operational accuracy, repeatability, and throughput.\n\n\n\nThe economic value AI applications could bring to the pharmaceutical industry is estimated to be between $350 billion and $410 billion annually by 2025.6 This integration not only ensures the timely delivery of medications but also maintains the integrity of sensitive products, contributing to patient safety and health care efficiency.\n\n\n\nNoteworthy Pharma AI Investments in 2023\n\nSeveral major pharmaceutical companies have made significant investments in AI, illustrating the industry's commitment to leveraging AI's emerging capabilities:\n\nSanofi. The French pharma giant rolled out its AI app, plai, which aggregates the company's internal data across all activities and functions. Sanofi uses AI to accelerate mRNA research and identify clinical trial sites for more diverse participation. Sanofi's CEO, Paul Hudson, emphasized their goal to become \"the first pharma company powered by artificial intelligence at scale.\"7\n\nAlexion AstraZeneca Rare Disease. In collaboration with Verge Genomics, Alexion AstraZeneca is leveraging machine learning to identify drug targets for rare neurodegenerative and neuromuscular diseases. This 4-year, multi-target deal exemplifies AI's potential in target identification and the acceleration of drug discovery for complex conditions.7\n\nBoehringer Ingelheim. Partnering with Phenomic AI, Boehringer Ingelheim is using AI and machine learning to discover stroma-rich cancer targets. Their use of AI to analyze single-cell RNA datasets for novel therapeutic targets showcases the potential of AI in oncology research.7\n\nPfizer. As an early adopter of Google Cloud's Target and Lead Identification Suite, Pfizer uses AI-powered tools to streamline the drug discovery process. This partnership highlights how tech giants and pharmaceutical companies collaborate to harness AI's power in drug development.7\n\nNovartis. As an investor in generative AI company Yseop, Novartis aims to automate the entire documentation process from preclinical trials through FDA approval. In 2023 alone, Yseop generated over 10,000 reports, demonstrating how AI can eliminate thousands of hours of writing and review time.7\n\n\n\nThese investments underscore the increasing recognition of AI's value in the pharmaceutical industry, from drug discovery to clinical trial management and beyond.\n\n\n\nEthical Considerations and Challenges\n\nWhile the potential benefits of using generative AI and chatbots in the pharmaceutical industry are immense, it is crucial to address the ethical considerations and challenges associated with these technologies:\n\nData privacy and security. The use of AI in clinical trials and patient communication requires access to sensitive medical data. Ensuring the privacy and security of this data is of utmost importance. Pharmaceutical companies must implement robust data protection measures and comply with regulations such as GDPR and HIPAA.3\n\nBias and fairness. AI algorithms can inadvertently perpetuate biases present in the training data. It is essential to develop and deploy AI systems that are fair and unbiased, ensuring that all patient populations are treated equitably.5 This issue is particularly critical given the diverse populations affected by different diseases.\n\nTransparency and accountability. The decisions made by AI systems can significantly impact patient care and trial outcomes. It is crucial to maintain transparency in how these systems operate and ensure accountability for their decisions. Clear guidelines and oversight mechanisms should be established to monitor AI-driven processes.2\n\nIntegration with existing systems. Integrating AI tools with existing clinical trial and health care systems can be challenging. Pharmaceutical companies must invest in training and infrastructure to ensure seamless integration and maximize the benefits of these technologies.6\n\nLack of transparency and interpretability. AI models, especially deep learning models, are often seen as \"black boxes\" due to their complexity. This lack of transparency can make it challenging to interpret the results and ensure that AI systems make decisions based on sound reasoning. This is particularly important in the pharmaceutical industry, where decisions can have life-altering consequences.3\n\nLimited ability to account for variability. AI models sometimes struggle to account for the complexity and variability of biological systems. Pharmaceutical companies must combine AI-based models with traditional experimental methods to ensure the safety and efficacy of drugs.4\n\nRegulatory and ethical compliance. AI applications must comply with existing regulatory standards, including those for clinical trials and drug approval. The ethical implications of using AI in health care, such as informed consent and the potential for unintended consequences, must be carefully considered.5\n\n\n\nConcluding Thoughts\n\nThe integration of LLMs, generative AI, and chatbots into the pharmaceutical industry represents a significant leap forward in how clinical trials are conducted and how information about medications is communicated. As these technologies continue to evolve, their impact will only grow, leading to more efficient research processes, improved patient outcomes, and more effective communication strategies.\n\n\n\nPharmaceutical companies that embrace these innovations will be better positioned to navigate the complexities of clinical trials and product launches in the modern era. By leveraging AI's capabilities, these companies can ensure that their research is not only cutting-edge but also patient-centric and responsive to the needs of health care professionals and patients alike.\n\n\n\nHowever, this transformation comes with challenges that must be addressed. Ethical considerations, data privacy, transparency, and integration with existing systems are all crucial factors that must be managed to harness AI's full potential responsibly. By doing so, pharmaceutical companies can usher in a new era of efficiency, effectiveness, and patient engagement, revolutionizing health care as we know it.\n\n\n\nAs we look to the future, it is imperative for the pharmaceutical industry to continue exploring the applications of AI while remaining vigilant about its ethical implications. The potential of AI to revolutionize drug development, improve patient outcomes, and lead to a more efficient and innovative health care system is immense. By addressing the challenges and leveraging AI’s capabilities, the pharmaceutical industry stands on the brink of a new era of discovery and advancement.\n\n\n\nDr Hyler is professor emeritus of psychiatry at Columbia University Medical Center.\n\n\n\nReferences\n\n1. Woo M. An AI boost for clinical trials. Nature. 2019;573(7775):S100-S102.\n\n2. Gholap AD, Uddin MJ, Faiyazuddin M, et al. Advances in artificial intelligence for drug delivery and development: a comprehensive review. Comput Biol Med. 2024;178:108702.\n\n3. Varol D. AI in the pharmaceutical industry: innovations and challenges. Scilife. January 30, 2024. Accessed October 14, 2024. https://www.scilife.io/blog/ai-pharma-innovation-challenges\n\n4. Srivastava V, Parveen B, Parveen R. Artificial intelligence in drug formulation and development: applications and future prospects. Curr Drug Metab. 2023;24(9):622-634.\n\n5. Singh S, Kumar R, Payra S, Singh SK. Artificial intelligence and machine learning in pharmacological research: bridging the gap between data and drug discovery. Cureus. 2023;15(8):e44359.\n\n6. Shah B, Viswa CA, Zurkiya D, et al. Generative AI in the pharmaceutical industry: moving from hype to reality. January 9, 2024. Accessed October 14, 2024. https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality\n\n7. Pecci A. 5 noteworthy pharma AI investments in 2023. Pharmavoice. December 19, 2023. Accessed October 14, 2024. https://www.pharmavoice.com/news/pharma-ai-investment-pfizer-sanofi-novartis/702882/\n\n","description":"Learn more about how AI can transform mental health care. ","author":[{"@type":"Person","name":"Steven E. 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As these technologies continue to evolve, their impact will only grow, leading to more efficient research processes, improved patient outcomes, and more effective communication strategies.","_key":"49c18d2cd6eb0"}],"_type":"block","upload_doc":null,"uploadAudio":null,"medias":null},{"medias":null,"markDefs":[],"children":[{"_key":"aa769910b9cb0","_type":"span","marks":[],"text":""}],"_type":"block","style":"normal","_key":"d8344ec57109","upload_doc":null,"uploadAudio":null},{"upload_doc":null,"uploadAudio":null,"medias":null,"style":"normal","_key":"f10ea43d888c","markDefs":[],"children":[{"_type":"span","marks":[],"text":"Pharmaceutical companies that embrace these innovations will be better positioned to navigate the complexities of clinical trials and product launches in the modern era. 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By doing so, pharmaceutical companies can usher in a new era of efficiency, effectiveness, and patient engagement, revolutionizing health care as we know it."}],"_type":"block","upload_doc":null,"uploadAudio":null},{"upload_doc":null,"uploadAudio":null,"medias":null,"_type":"block","style":"normal","_key":"02482cc5b30c","markDefs":[],"children":[{"_type":"span","marks":[],"text":"","_key":"ed309d3e4c850"}]},{"_type":"block","style":"normal","_key":"64b9f30c21b4","upload_doc":null,"uploadAudio":null,"medias":null,"markDefs":[],"children":[{"_type":"span","marks":[],"text":"As we look to the future, it is imperative for the pharmaceutical industry to continue exploring the applications of AI while remaining vigilant about its ethical implications. The potential of AI to revolutionize drug development, improve patient outcomes, and lead to a more efficient and innovative health care system is immense. 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When I read the article, I was disappointed in my interview, and I asked the director of education at my fellowship if the department would enroll me in a media training course. My director’s response was a Socratic one. He said, “Judith, you should develop the course yourself. And then when you finish writing the course, you should teach it to the rest of us.”","_key":"18059a9abd7a","_type":"span","marks":[]}],"_type":"block","style":"normal","_key":"c077a915361c"},{"_type":"block","style":"normal","_key":"574ca280f80c","markDefs":[],"children":[{"_type":"span","marks":[],"text":"","_key":"01d2e45e949d0"}]},{"markDefs":[],"children":[{"_key":"ebaa805a2d070","_type":"span","marks":[],"text":"Initially I thought that he was joking, but he wasn’t. In fact, he suggested that I focus on media training as the topic of my second year senior project during my fellowship program. 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I had no idea that I would have this much personal and career success in such a short period of time. My social media reels went viral, and within 2 years I had almost 1 million followers across TikTok, Instagram, Facebook, and Threads. My social media advocacy and research led me to receiving a US House of Representatives Proclamation in June 2023. I was nominated by Congresswoman Sheila Cherfilus-McCormick in honor of Caribbean American Heritage Month and invited to Vice President Kamala Harris’s reception at the White House after my proclamation was read before Congress. 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Black women have some of the scariest rates of postpartum depression, postpartum suicide, postpartum complications, perinatal death, and negative perimenopause/menopause outcomes.","_key":"3b59510c15200"}],"_type":"block","style":"normal","_key":"2557a9712e69","markDefs":[]},{"_type":"block","style":"normal","_key":"039245058620","markDefs":[],"children":[{"_type":"span","marks":[],"text":"My patients who experience physical and mental health symptoms related to menopause benefit from hormone replacement therapy (HRT) and other interventions like psychotherapy and cognitive behavioral therapy for sleep. Some patients benefit from antidepressants and sleep aides. However, most people are not offered HRT, and this is problematic. According to a 2019 Mayo Clinic survey of resident physicians, only 6.8% felt adequately prepared for managing the treatment of women in menopause and only 58% of the participants had up to one lecture about menopause during residency training. This gap of knowledge in medical providers plays a large role in the lack of menopause competency in health care.","_key":"a118c0bb2dfb0"}]},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"In addition, many patients going through perimenopause and menopause are misdiagnosed as having major depressive disorders because of the similarity of the symptoms. They present to their doctors with cognitive concerns, identity loss, sadness, moodiness, anxiety and insomnia—symptoms that may occur in both major depressive disorder and in perimenopause. Often, these patients do not have a primary major depressive episode; they are experiencing mood symptoms related to hormonal fluctuations.","_key":"75462d0aba030"}],"_type":"block","style":"normal","_key":"85f8070b6ef7"},{"children":[{"text":"The disconnect in physician knowledge is largely due to the fact that the 2002 Women’s Health Initiative study was misleading and concluded that HRT had risks that far outweighed the benefits. This statement has since been debunked, but the message that HRT was harmful was so pervasive in health care that doctors stopped prescribing it and patients have suffered as a result.","_key":"a6bda2f80f0b0","_type":"span","marks":[]}],"_type":"block","style":"normal","_key":"68d5e526c0a7","markDefs":[]},{"_type":"block","style":"normal","_key":"3911824cedbc","markDefs":[],"children":[{"_type":"span","marks":[],"text":"Let’s Talk Menopause educates women for free via webinars, events, and social media content. In addition, advocacy is a large part of our mission and activities. We recently advocated alongside Congresswoman Yvette Clarke in front of Congress to support the MREA (Menopause Research and Equity Act).I also developed the T.I.E.S. Method to help patients identify the mental health symptoms of menopause and how to discern them from primary mental health conditions.","_key":"b72334b6bef50"}]},{"markDefs":[],"children":[{"marks":[],"text":"","_key":"28409203583c0","_type":"span"}],"_type":"block","style":"normal","_key":"95a8b3fabf3e"},{"children":[{"text":"PT","_key":"75ba7349ed7a0","_type":"span","marks":["strong","em"]},{"marks":["strong"],"text":": On the other end of the spectrum, much has been said about the mental health crisis in youth. As a clinical psychiatrist, what has been your experience?","_key":"75ba7349ed7a1","_type":"span"}],"_type":"block","style":"normal","_key":"41b547e6e830","markDefs":[]},{"_type":"block","style":"normal","_key":"24e482e941e7","markDefs":[],"children":[{"_type":"span","marks":["strong"],"text":"Joseph:","_key":"31fe8ef437080"},{"_type":"span","marks":[],"text":" I am a child psychiatrist who also treats adults, and can report that this issue is not just in children, it is happening across all ages. The post-pandemic mental health crisis seems to be worsening and people do not have access to quality mental health care.","_key":"e64a683ac40e"}]},{"style":"normal","_key":"998b3e35507b","markDefs":[],"children":[{"text":"","_key":"756c4f13676e0","_type":"span","marks":[]}],"_type":"block"},{"markDefs":[],"children":[{"_key":"b6836ef4c4020","_type":"span","marks":[],"text":"Recently I have been working with US Representative Ro Khanna and other mental health advocates to address the crisis in youth. One of the suggestions that I have made is to focus on funding mental health services and education in schools. We need to approach this from a preventative approach and teach children how to use social media responsibly, how to regulate their mood, and how to determine if relationships are harmful."}],"_type":"block","style":"normal","_key":"ba29eadbe0eb"},{"_type":"block","style":"normal","_key":"00f88c169332","markDefs":[],"children":[{"marks":[],"text":"","_key":"31b7e8a218f90","_type":"span"}]},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"We need to provide these types of workshops in the workplace for adults as well. Preventing mental health issues is where we should focus our efforts, because the reality is that it will take decades to create and disseminate new therapists and mental health professionals. The shortage of professionals is far too great, and 1 in 3 Americans lives in an area where there is a shortage of these professionals.","_key":"db51df7e05410"}],"_type":"block","style":"normal","_key":"a8c03b46f2fc"},{"_type":"block","style":"normal","_key":"cc701a202d68","markDefs":[],"children":[{"text":"","_key":"287e361324590","_type":"span","marks":[]}]},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"Moreover, minority mental health is often overlooked in adults and youth. There are very few Black therapists compared with other therapists, and this creates a barrier to care. For this reason, I create social media content that directly addresses Black patient populations so that they are informed and are receiving support and resources from a cultural competency perspective.","_key":"fcf35e8838720"}],"_type":"block","style":"normal","_key":"cb006519ac6a"},{"children":[{"_type":"span","marks":[],"text":"","_key":"f7d986326df50"}],"_type":"block","style":"normal","_key":"8690aa8406d9","markDefs":[]},{"markDefs":[],"children":[{"marks":[],"text":"When I was a resident, I received a SAMHSA minority health grant to focus on cultural competency in the Black community, and I use what I learned during that grant period to create evidence-based content to support my community. I have also done several events with the Congressional Black Caucus around Black mental health in children and adults.","_key":"f78c173f7de20","_type":"span"}],"_type":"block","style":"normal","_key":"7017cd99ac80"},{"_key":"621f0056d6d5","markDefs":[],"children":[{"marks":[],"text":"","_key":"291075d4df750","_type":"span"}],"_type":"block","style":"normal"},{"_type":"block","style":"normal","_key":"d7cad6f1f61b","markDefs":[],"children":[{"_type":"span","marks":["strong","em"],"text":"PT","_key":"5ca00746bbb10"},{"_type":"span","marks":["strong"],"text":": Another area of interest of yours is high functioning depression (HFD). Can you tell us more about this condition and what you have found in your research?","_key":"5ca00746bbb11"}]},{"markDefs":[],"children":[{"_type":"span","marks":["strong"],"text":"Joseph:","_key":"8bf40b307f750"},{"marks":[],"text":" I am very excited about my research in HFD. In this post-pandemic period, we are learning about patients who have hidden conditions, like HFD which is often masked by busyness.","_key":"917866e55ee1","_type":"span"}],"_type":"block","style":"normal","_key":"2a8c6f34a366"},{"_type":"block","style":"normal","_key":"00e575966039","markDefs":[],"children":[{"_type":"span","marks":[],"text":"HFD affects highly visible successful figures as well as everyday individuals. People with high functioning depression may include the chief executive officer who runs her company and masks her symptoms in light of grave financial uncertainty; or the famous sports figure who has to bring home a win for his franchise. Think of the teacher who works through lunch to grade his students’ papers on time so that they pass the state exams or the single working mom who stretches the dollar to make sure her kids are fed and gets them to school on time every day despite a lack of support. I also think that many health care professionals, specifically doctors, suffer from HFD.","_key":"78331582b4530"}]},{"style":"normal","_key":"a0316b5796ca","markDefs":[],"children":[{"_type":"span","marks":[],"text":"People with HFD push through painful situations to meet or exceed their performance expectations. Those with HFD often report symptoms of anhedonia. Although individuals with HFD have some symptoms of depression (eg, poor sleep, low energy, or self-doubt), their symptoms do not meet the ","_key":"d32509872d980"},{"marks":["em"],"text":"DSM 5","_key":"d32509872d981","_type":"span"},{"_type":"span","marks":[],"text":" criteria for major depressive disorder because the individuals exceed basic functioning; they push through pain and they don’t always identify as being emotionally distressed. This is why HFD often goes unnoticed by medical providers and mental health professionals; clinicians are looking for a lack or loss of functioning, and these patients do not have that.","_key":"d32509872d982"}],"_type":"block"},{"style":"normal","_key":"cf29524b5d52","markDefs":[],"children":[{"_type":"span","marks":[],"text":"HFD is contagious, not in the way that a virus spreads, but in the way that unhealthy habits may rub off on those around us. Think about the boss who works themselves and their team to the bone, or the parent who never seems satisfied with their child’s academic progress; this can spread, if not contained. If we can contain HFD and its most insidious symptom, anhedonia, we can hopefully put an end to this epidemic and spread joy instead.","_key":"d175ddb4c5bc0"}],"_type":"block"},{"_key":"87abf4d58fad","markDefs":[],"children":[{"marks":[],"text":"My research and upcoming book on the topic shares evidence-based tools for combating symptoms of anhedonia so that life feels more meaningful and purposeful. The book and research findings are set to be released in March 2025. In the meantime, the High Functioning Blog on my website offers the high functioning depression scale and the anhedonia scale to learn more about the symptoms. I also have a free weekly newsletter that provides evidence-based tips on how to improve mental health.","_key":"3dbac6c153440","_type":"span"}],"_type":"block","style":"normal"},{"children":[{"text":"","_key":"a703bcb7db6b0","_type":"span","marks":[]}],"_type":"block","style":"normal","_key":"c239cd64843c","markDefs":[]},{"markDefs":[],"children":[{"marks":["strong","em"],"text":"PT","_key":"64893b4eabfb","_type":"span"},{"text":": Thank you! 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Accessed June 24, 2024. ","_key":"65dad39685810"},{"marks":["1b60ead29626"],"text":"https://shortyawards.com/8th-impact/immigrant-mental-health-awareness","_key":"65dad39685811","_type":"span"}],"_type":"block","style":"normal"},{"children":[{"text":"Joseph J. ","_key":"0cad1ff946b80","_type":"span","marks":[]},{"_type":"span","marks":["3b06c5648782"],"text":"High Functioning Depression Blog","_key":"0cad1ff946b81"},{"_key":"0cad1ff946b82","_type":"span","marks":[],"text":". Accessed June 24, 2024. 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The artist Laurie Anderson has even used AI to help with her grieving of her late husband, the rock icon Lou Reed, by conflating his work so that Laurie could then ask the algorithmic version of Reed questions.","_key":"ba2ed338b95d0","_type":"span"}],"_type":"block","style":"normal","_key":"a126ac783f51"},{"_type":"block","style":"normal","_key":"50ed1d2f0aba","markDefs":[],"children":[{"text":"","_key":"465526c48def0","_type":"span","marks":[]}]},{"children":[{"_type":"span","marks":[],"text":"AI would seem to have great psychiatric potential in understanding and diagnosing due to its pattern recognition ability. However, it can also be inaccurate and biased like humans, in large part because it uses our available knowledge, and that knowledge can also be slanted in certain directions by programmers. 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I coordinated consultations across New York State, connecting the New York State Psychiatric Institute/Columbia University host site with various state psychiatric centers, community mental health centers, and mental health units in maximum-security prisons. ","_key":"fd66294ae8310"}],"_type":"block","style":"normal"},{"style":"normal","_key":"074e766e2d5f","markDefs":[],"children":[{"_type":"span","marks":[],"text":"The emergence of big data in recent years, highlighted by IBM Watson’s triumph over Jeopardy champions, sparked my imagination about the possibilities of applying such technology to medicine and psychiatry. And now, with the advent of ChatGPT in early 2023, I find myself diving headfirst into the world of chatbots. ","_key":"dc897baaa7870"}],"_type":"block"},{"style":"normal","_key":"8f2bd99bacac","markDefs":[],"children":[{"_type":"span","marks":[],"text":"The idea for “Chatbot Corner” has been brewing in my mind for months. 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Whether it is sharing the most egregious chatbot errors you have encountered in practice or your experience with trying to stump the psychiatry chatbot with complex scenarios, your contributions are vital. ","_key":"2c977cb4cfa80"}],"_type":"block","style":"normal"},{"widthP":20,"_type":"figure","imgcaption":[{"_key":"deceebc7a284","markDefs":[],"children":[{"marks":[],"text":"Steven E. Hyler, MD","_key":"e5498a1640020","_type":"span"}],"_type":"block","style":"normal"}],"_key":"810a31adad58","alignment":"right","asset":{"_ref":"image-f47768af6e7020f3d45946779d41072bfde93240-776x1184-png","_type":"reference"}},{"markDefs":[],"children":[{"_key":"0a12e68b03960","_type":"span","marks":[],"text":"We welcome columns, commentaries, letters, and articles that delve into the ethical, practical, and clinical implications of AI in psychiatry. Be a part of shaping the future of mental health care in the age of AI. 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Despite training on general data, ChatGPT performs many medical and health care tasks well.","_key":"146ac42f0c7f0"},{"_type":"span","marks":["superscript"],"text":"6","_key":"5b2bbcb81c4e"},{"marks":[],"text":" Mental health clinicians may look to AI- and LLM-powered tools to help reduce administrative burden and provide clinical decision support. Patients may benefit from these tools for education, self-care, communication with their care teams, and more.","_key":"622dd095f2f8","_type":"span"}]},{"_type":"block","style":"normal","_key":"745b3f444eaf","markDefs":[],"children":[{"_type":"span","marks":[],"text":"The industry is optimistic in its applications, as studies have shown high accuracy for potential applications in clinical decision support. Deep learning models have shown high accuracy in predicting mental health disorder and severity.7 ChatGPT achieved final diagnosis accuracy of 76.9% (95% CI 67.8%-86.1%) in a study of 36 clinical vignettes.","_key":"f93c4f8a475f0"},{"text":"8","_key":"3a7df20979cd","_type":"span","marks":["superscript"]},{"_type":"span","marks":[],"text":" ","_key":"3fa0a193f069"}]},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"However, it is important to note that this study was carried out in an experimental environment with a small case sample. These vignettes may not truly approximate a patient’s real-world clinical presentation or the text input a user would naturally enter into the chatbot interface.","_key":"e0d36ea5f82c"}],"_type":"block","style":"normal","_key":"4fcad6832aed"},{"style":"normal","_key":"c8faaac3998a","markDefs":[],"children":[{"_type":"span","marks":[],"text":"Several organizations are piloting the application of ChatGPT, and outcomes of real-world deployment in clinical settings are missing.6,9 However, real-world clinical utilization of AI has not been without controversy. A little-known mental health app went viral when it replaced human responses with AI without telling patients.","_key":"f6cfb9fd63b90"},{"_key":"c23da907ef8f","_type":"span","marks":["superscript"],"text":"10"},{"_type":"span","marks":[],"text":" ","_key":"fb7333869f4e"}],"_type":"block"},{"markDefs":[],"children":[{"_key":"8058d58a446a","_type":"span","marks":[],"text":"The capabilities of the technology are evolving rapidly, and utilization and ethics must play catchup. Although accuracy increases and nonsensical, confabulated outputs are reduced with each subsequent model enhancement, major limitations and concerns with the tool persist."},{"_type":"span","marks":["superscript"],"text":"11","_key":"91dc57367ef2"}],"_type":"block","style":"normal","_key":"b063cb2d4cae"},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"","_key":"f23654761d760"}],"_type":"block","style":"normal","_key":"f8e6e5e940b5"},{"style":"normal","_key":"cc4069c97e66","markDefs":[],"children":[{"text":"Key Limitations of Generative Language Models","_key":"820c8df2c4460","_type":"span","marks":["strong"]}],"_type":"block"},{"style":"normal","_key":"76a4b8f02b6a","markDefs":[],"children":[{"_type":"span","marks":[],"text":"Despite the enthusiasm for the potential impact AI can make in psychiatry, the industry is cautious and slow to implement the technology, given its limitations. Although there are many potential limitations, common concerns include accuracy, quality, and transparency of training data; confabulated outputs (“hallucinations”); and biases.","_key":"75be5ad9bc010"}],"_type":"block"},{"style":"normal","_key":"4afd415b8ef2","markDefs":[],"children":[{"_key":"0032bffe2ca50","_type":"span","marks":[],"text":"Diagnosis accuracy can be high on vignette studies—however, these rates dip when case complexity rises. One clinical vignette study showed that ChatGPT-4 achieved 100% diagnosis accuracy within the top 3 suggested diagnoses for common cases, while human medical doctors solved 90% within the top 2 suggestions, but did not reach 100% with up to 10 suggestions. "}],"_type":"block"},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"For rare cases, only 40% are solved within the first diagnosis suggestion and it takes 8 or more suggestions to achieve 90% diagnosis accuracy. 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ChatGPT was trained on a large corpus of text data, such as web pages and books.2 If the GPT model does not handle high- and low-quality evidence differently, this can lead to incorrect or inaccurate outputs.","_key":"970c4f599e140","_type":"span"},{"_type":"span","marks":["superscript"],"text":"13","_key":"3b30ba3574b0"},{"_type":"span","marks":[],"text":" With billions of parameters, model retraining requires intensive, expensive computation.11 ChatGPT-3.5 (the free version currently available to the public) uses training data from up to September 2021, and it cannot access or leverage real-time data and events from after this date.","_key":"56a60e30c596"},{"marks":["superscript"],"text":"14","_key":"4ac69138b081","_type":"span"}],"_type":"block","style":"normal","_key":"faf3c4c96cb6"},{"_type":"block","style":"normal","_key":"c070741829cf","markDefs":[],"children":[{"text":"LLMs can generate confabulated outputs known as hallucinations. Hallucinations are particularly concerning in medical settings, where incorrect information may have implications for morbidity and mortality. ChatGPT may make a correct choice but give nonsensical explanations. In 1 study, ChatGPT constructed false references within a research paper.","_key":"abe613541d950","_type":"span","marks":[]},{"_key":"3f13918b6fa5","_type":"span","marks":["superscript"],"text":"15"},{"_type":"span","marks":[],"text":" ","_key":"1984476578a7"}]},{"_type":"block","style":"normal","_key":"a22ec941115c","markDefs":[],"children":[{"_type":"span","marks":[],"text":"In another study, when asked to answer a clinical question about the feasibility, safety, and effectiveness of an implantable cardioverter-defibrillator (ICD), ChatGPT referred to 2 different studies that do not exist.","_key":"ada0c3a1a267"},{"text":"16","_key":"adb0694612b2","_type":"span","marks":["superscript"]},{"_type":"span","marks":[],"text":" ChatGPT may also hallucinate medical knowledge or mathematical deductions.","_key":"a2198cc218aa"},{"marks":["superscript"],"text":"11","_key":"689850b28db6","_type":"span"}]},{"style":"normal","_key":"ac20f1498aef","markDefs":[],"children":[{"_type":"span","marks":[],"text":"Biases from the training data—like those regarding gender, race, and culture—make ChatGPT capable of generating stereotyped and prejudiced content.","_key":"c62c8a2b7b730"},{"_type":"span","marks":["superscript"],"text":"2,17","_key":"5c3c00a3d9c1"},{"_type":"span","marks":[],"text":" In its initial release, when asked to identify who would be a good scientist based on race and gender, ChatGPT said a “white male” and responded negatively for all other subgroups.18","_key":"40d7cbf667ee"}],"_type":"block"},{"_key":"3a44c0962a4d","markDefs":[],"children":[{"text":"","_key":"3c7d7170e0190","_type":"span","marks":[]}],"_type":"block","style":"normal"},{"_key":"17f8a59e0a97","markDefs":[],"children":[{"_key":"9238e00c79030","_type":"span","marks":["strong"],"text":"Combatting Limitations With Custom GPTs"}],"_type":"block","style":"normal"},{"style":"normal","_key":"7c58b56cbc4f","markDefs":[],"children":[{"_type":"span","marks":[],"text":"In November 2023, OpenAI introduced custom GPTs as a powerful new feature of their flagship product, ChatGPT. The custom GPT feature allows individual users to tailor the large language model to their specific parameters using plain language prompts. This new feature allows users to input datasets and resources while also telling the custom GPT which references should be utilized in responses. ","_key":"f74904fc3a210"}],"_type":"block"},{"_key":"6c1650b0c637","markDefs":[],"children":[{"_type":"span","marks":[],"text":"In medicine, AI has demonstrated promise in its ability to correctly answer medical questions, but what currently limits wider adoption in clinical decision-making is the risk of hallucinations and inaccurate answers.","_key":"22b4425bb8b2"}],"_type":"block","style":"normal"},{"style":"normal","_key":"382745165740","markDefs":[],"children":[{"_type":"span","marks":[],"text":"Large language models do not differentiate between the validity of their sources, but rather aggregate the best response using word patterns. In medicine, a response generated by a peer-reviewed journal reference is considered higher quality and more reliable and very different from one generated by sources in an internet chat room. The custom GPT builder works to address this issue.","_key":"cbb1f77f32320"}],"_type":"block"},{"_key":"e50f4fa3d79b","markDefs":[],"children":[{"marks":[],"text":"","_key":"0902acebf8a60","_type":"span"}],"_type":"block","style":"normal"},{"_key":"31115dc9868e","markDefs":[],"children":[{"text":"Methods","_key":"e1d119fac6810","_type":"span","marks":["strong"]}],"_type":"block","style":"normal"},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"Neuro Scholar, a custom GPT on the OpenAI ChatGPT platform, was created with a focus on neuroscience and psychiatry. Neuro Scholar was instructed to communicate in a formal tone, reflecting an academic and professional nature suitable for discussing complex, evidence-based topics in neuroscience and psychiatry. It is designed to ask clarifying questions when needed to ensure accurate and relevant responses. ","_key":"4451c9d96f5c0"}],"_type":"block","style":"normal","_key":"47ee0faafb69"},{"children":[{"text":"Neuro Scholar focuses on providing comprehensive, evidence-based information on neuroscience and psychiatry topics, strictly adhering to scientific literature that was uploaded to the reference collection. The model was also instructed to notify the user if the answer was not available in the uploaded resources and to warn the user if generalized knowledge (references outside the uploaded dataset) was utilized.","_key":"5bbeb98d9144","_type":"span","marks":[]}],"_type":"block","style":"normal","_key":"5a0507fcd872","markDefs":[]},{"children":[{"_type":"span","marks":[],"text":"The Neuro Scholar reference collection includes textbooks and other resources that encompass a wide range of topics in neuroscience, psychiatry, and related fields. 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The practice exam was administered to Neuro Scholar and ChatGPT-3.5.","_key":"5cae418763114"}]},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"","_key":"a4bae55769790"}],"_type":"block","style":"normal","_key":"2d28ee4210c2"},{"markDefs":[],"children":[{"_type":"span","marks":["strong"],"text":"Results","_key":"a51f0e7abe970"}],"_type":"block","style":"normal","_key":"a82d6ce70ca3"},{"markDefs":[{"_type":"link","href":"https://www.psychiatrictimes.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F0vv8moc6%2Fpsychtimes%2Fa719b8e87dc57a3b5ff54fad1b59b3f9876e0b52-936x578.png%3Ffit%3Dcrop%26auto%3Dformat\u0026w=1920\u0026q=75","_key":"27f11251508d","nofollow":true,"blank":true}],"children":[{"_type":"span","marks":[],"text":"ChatGPT-3.5 correctly answered 125 out of 150 questions, while Neuro Scholar correctly answered 145 out of 150 questions, achieving 96.67% accuracy on the practice exam (","_key":"8354b68247f30"},{"_type":"span","marks":["strong","27f11251508d"],"text":"Figure","_key":"8354b68247f31"},{"_type":"span","marks":[],"text":"). ","_key":"8354b68247f32"}],"_type":"block","style":"normal","_key":"0d0db19567aa"},{"alignment":"right","asset":{"_ref":"image-a719b8e87dc57a3b5ff54fad1b59b3f9876e0b52-936x578-png","_type":"reference"},"widthP":50,"blank":true,"_type":"figure","alt":"Figure. ChatGPT 3.5 vs Neuro Scholar Custom GPT Performance in the ABPN Practice Exam","imgcaption":[{"_type":"block","style":"normal","_key":"a74e46bf2924","markDefs":[],"children":[{"text":"Figure. ","_key":"8b2db2c7d43b0","_type":"span","marks":["strong"]},{"marks":[],"text":"ChatGPT 3.5 vs Neuro Scholar Custom GPT Performance in the ABPN Practice Exam","_key":"d6dd89e34b9f","_type":"span"}]}],"_key":"e92857151330"},{"children":[{"_type":"span","marks":[],"text":"The 5 questions that Neuro Scholar answered incorrectly are listed in ","_key":"5a351bff63fe"},{"_type":"span","marks":["strong","a294159b2c90"],"text":"Table 3","_key":"8354b68247f33"},{"_type":"span","marks":[],"text":"; ChatGPT-3.5 also missed these questions.","_key":"8354b68247f34"}],"_type":"block","style":"normal","_key":"53fb1f0d169c","markDefs":[{"blank":true,"_type":"link","href":"https://www.psychiatrictimes.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F0vv8moc6%2Fpsychtimes%2F533952969a7b5e6ede1fb92b26c409b6574767ff-1602x1148.png%3Ffit%3Dcrop%26auto%3Dformat\u0026w=3840\u0026q=75","_key":"a294159b2c90","nofollow":true}]},{"_key":"9dce94c6f132","alignment":"right","asset":{"_ref":"image-533952969a7b5e6ede1fb92b26c409b6574767ff-1602x1148-png","_type":"reference"},"widthP":50,"blank":true,"_type":"figure","alt":"Table 3. Questions Answered Incorrectly by Neuro Scholar Custom GPT","imgcaption":[{"_key":"094feb20b5d8","markDefs":[],"children":[{"_type":"span","marks":["strong"],"text":"Table 3.","_key":"442b0e13f122"},{"_type":"span","marks":[],"text":" Questions Answered Incorrectly by Neuro Scholar Custom GPT","_key":"986d3bac3599"}],"_type":"block","style":"normal"}]},{"markDefs":[],"children":[{"marks":["strong"],"text":"Discussion","_key":"566b15f324a90","_type":"span"}],"_type":"block","style":"normal","_key":"f5fc1c36800a"},{"children":[{"_type":"span","marks":[],"text":"In just 1 year, AI and large language models have rapidly evolved and are reaching a tipping point with regards to educational and clinical viability. The above proof-of-concept experiment demonstrates that customized generative AI can improve accuracy and reduce hallucinations through control of which resources the model utilizes. In medicine, AI hallucinations can have disastrous consequences. Efforts to improve AI accuracy must also include efforts to eliminate inaccurate responses.","_key":"0ec2d83528760"}],"_type":"block","style":"normal","_key":"ab07b96fcbd4","markDefs":[]},{"_type":"block","style":"normal","_key":"2e38fa4b5a99","markDefs":[],"children":[{"_type":"span","marks":[],"text":"This proof-of-concept experiment also highlights concerns regarding intellectual property ownership within AI models. The Neuro Scholar custom GPT was built using multiple psychiatry textbooks that were acquired through the secondary market. ","_key":"c0235892bf970"}]},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"Recently, OpenAI announced a partnership with publisher Axel Springer.","_key":"4928cb1fd10a"},{"_type":"span","marks":["superscript"],"text":"19","_key":"b0536ad1013b"},{"_key":"b99d4bace62f","_type":"span","marks":[],"text":" This partnership will formalize OpenAI’s use of the publisher’s content as resources for generative responses. As AI continues to evolve, further guidance will be needed regarding the use of copyrighted materials such as journal articles, textbooks, and reference guides."}],"_type":"block","style":"normal","_key":"b1339a38f04d"},{"style":"normal","_key":"aee8af4b631f","markDefs":[],"children":[{"marks":[],"text":"Plain language programing of custom AI models carries many exciting implications in the world of psychiatry. In medical education, students can study by using AI prompts with responses that are focused on the specific learning concept. As a tool for clinical support, custom AI models can be configured to provide algorithms that reference all available treatment guidelines for a respective condition. Lastly, industry within psychiatry can harness these models to provide medical affairs information while controlling the specific resources referenced.","_key":"efdb67115ec90","_type":"span"}],"_type":"block"},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"","_key":"dc137f41e5ae0"}],"_type":"block","style":"normal","_key":"186c23fcce7b"},{"children":[{"_key":"5d417b74cd360","_type":"span","marks":["strong"],"text":"Concluding Thoughts"}],"_type":"block","style":"normal","_key":"481e60ede781","markDefs":[]},{"_type":"block","style":"normal","_key":"f2ec4be918fa","markDefs":[],"children":[{"_type":"span","marks":[],"text":"AI was the transformative business development of 2023, and it will continue to evolve and change the landscape of medicine and psychiatry. As clinicians, it is imperative for us to understand the potential of AI in psychiatry, but also to understand and remain wary of its limitations. AI may help psychiatry address chronic problems including adherence to treatment guidelines, access shortages, and didactic education across multiple professions and training settings.","_key":"e0a963c9bafb0"}]},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"","_key":"d9bc4dd13c1e0"}],"_type":"block","style":"normal","_key":"7f0d1601831d"},{"_type":"block","style":"normal","_key":"87fa7e68f765","markDefs":[],"children":[{"_type":"span","marks":["strong"],"text":"Mr Asbach","_key":"4eda1ae3bc220"},{"marks":[],"text":" ","_key":"4eda1ae3bc221","_type":"span"},{"_key":"4eda1ae3bc222","_type":"span","marks":["em"],"text":"is a psychiatric physician associate and serves as associate director of interventional psychiatry at DENT Neurologic Institute. "},{"_type":"span","marks":["strong"],"text":"Ms Menon ","_key":"4eda1ae3bc223"},{"_type":"span","marks":["em"],"text":"is an independent researcher who is currently freelancing in advisory and contract roles developing clinical AI products. ","_key":"4eda1ae3bc225"},{"marks":["strong"],"text":"Mr Long","_key":"4eda1ae3bc226","_type":"span"},{"_type":"span","marks":["em"],"text":" is a PA student at Daemen University in Amherst, New York.","_key":"4eda1ae3bc227"}]},{"markDefs":[],"children":[{"_type":"span","marks":[],"text":"","_key":"dd0667d6cf180"}],"_type":"block","style":"normal","_key":"f10c344dc257"},{"children":[{"text":"References","_key":"d7abcc0aa51b0","_type":"span","marks":["strong"]}],"_type":"block","style":"normal","_key":"b0f0395bea33","markDefs":[]},{"_key":"b323fa85d372","markDefs":[{"blank":true,"_type":"link","href":"https://pubmed.ncbi.nlm.nih.gov/37612880/","_key":"dcf0c0e4251e"}],"children":[{"_key":"17365b6e9cf70","_type":"span","marks":[],"text":"1. 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The economic potential of generative AI: the next productivity frontier. McKinsey Digital. June 14, 2023. Accessed December 30, 2023.","_key":"24525cbab4220","_type":"span","marks":[]},{"_type":"span","marks":["72648f558c49"],"text":"https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction","_key":"24525cbab4221"}],"_type":"block","style":"normal"},{"style":"normal","_key":"72d85aa41f56","markDefs":[{"blank":true,"_type":"link","href":"https://pubmed.ncbi.nlm.nih.gov/36981544/","_key":"ce38f2982187"}],"children":[{"_type":"span","marks":[],"text":"5. Sallam M. ","_key":"49fc2c77a16c0"},{"_type":"span","marks":["ce38f2982187"],"text":"ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns","_key":"49fc2c77a16c1"},{"_type":"span","marks":[],"text":". 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