Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks
Large language models (LLMs) are transforming the capabilities of medical chatbots by enabling more context-aware, human-like interactions. This review presents a comprehensive analysis of their applications, technical foundations, benefits, challenges, and future directions in healthcare. LLMs are...
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MDPI AG
2025-06-01
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| author | James C. L. Chow Kay Li |
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| description | Large language models (LLMs) are transforming the capabilities of medical chatbots by enabling more context-aware, human-like interactions. This review presents a comprehensive analysis of their applications, technical foundations, benefits, challenges, and future directions in healthcare. LLMs are increasingly used in patient-facing roles, such as symptom checking, health information delivery, and mental health support, as well as in clinician-facing applications, including documentation, decision support, and education. However, as a study from 2024 warns, there is a need to manage “extreme AI risks amid rapid progress”. We examine transformer-based architectures, fine-tuning strategies, and evaluation benchmarks specific to medical domains to identify their potential to transfer and mitigate AI risks when using LLMs in medical chatbots. While LLMs offer advantages in scalability, personalization, and 24/7 accessibility, their deployment in healthcare also raises critical concerns. These include hallucinations (the generation of factually incorrect or misleading content by an AI model), algorithmic biases, privacy risks, and a lack of regulatory clarity. Ethical and legal challenges, such as accountability, explainability, and liability, remain unresolved. Importantly, this review integrates broader insights on AI safety, drawing attention to the systemic risks associated with rapid LLM deployment. As highlighted in recent policy research, including work on managing extreme AI risks, there is an urgent need for governance frameworks that extend beyond technical reliability to include societal oversight and long-term alignment. We advocate for responsible innovation and sustained collaboration among clinicians, developers, ethicists, and regulators to ensure that LLM-powered medical chatbots are deployed safely, equitably, and transparently within healthcare systems. |
| format | Article |
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| publishDate | 2025-06-01 |
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| spelling | doaj-art-13ed5d3bbd384bf6b92a1e546a8c19b02025-07-25T13:25:04ZengMDPI AGInformation2078-24892025-06-0116754910.3390/info16070549Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI RisksJames C. L. Chow0Kay Li1Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1X6, CanadaDepartment of English, University of Toronto, Toronto, ON M5R 0A3, CanadaLarge language models (LLMs) are transforming the capabilities of medical chatbots by enabling more context-aware, human-like interactions. This review presents a comprehensive analysis of their applications, technical foundations, benefits, challenges, and future directions in healthcare. LLMs are increasingly used in patient-facing roles, such as symptom checking, health information delivery, and mental health support, as well as in clinician-facing applications, including documentation, decision support, and education. However, as a study from 2024 warns, there is a need to manage “extreme AI risks amid rapid progress”. We examine transformer-based architectures, fine-tuning strategies, and evaluation benchmarks specific to medical domains to identify their potential to transfer and mitigate AI risks when using LLMs in medical chatbots. While LLMs offer advantages in scalability, personalization, and 24/7 accessibility, their deployment in healthcare also raises critical concerns. These include hallucinations (the generation of factually incorrect or misleading content by an AI model), algorithmic biases, privacy risks, and a lack of regulatory clarity. Ethical and legal challenges, such as accountability, explainability, and liability, remain unresolved. Importantly, this review integrates broader insights on AI safety, drawing attention to the systemic risks associated with rapid LLM deployment. As highlighted in recent policy research, including work on managing extreme AI risks, there is an urgent need for governance frameworks that extend beyond technical reliability to include societal oversight and long-term alignment. We advocate for responsible innovation and sustained collaboration among clinicians, developers, ethicists, and regulators to ensure that LLM-powered medical chatbots are deployed safely, equitably, and transparently within healthcare systems.https://www.mdpi.com/2078-2489/16/7/549large language modelsmedical chatbotsgenerative artificial intelligencenatural language processingclinical decision supporthealthcare AI governance |
| spellingShingle | James C. L. Chow Kay Li Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks Information large language models medical chatbots generative artificial intelligence natural language processing clinical decision support healthcare AI governance |
| title | Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks |
| title_full | Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks |
| title_fullStr | Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks |
| title_full_unstemmed | Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks |
| title_short | Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks |
| title_sort | large language models in medical chatbots opportunities challenges and the need to address ai risks |
| topic | large language models medical chatbots generative artificial intelligence natural language processing clinical decision support healthcare AI governance |
| url | https://www.mdpi.com/2078-2489/16/7/549 |
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