Large Language Model-based Chatbots and Medical Regulation
Apr 16, 2024
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Prof. Stephen Gilbert, a leading expert in Medical Device Regulatory Science, dives into the intersection of AI and medical regulation. He discusses the challenges of adapting traditional frameworks for AI chatbots and large language models, emphasizing the need for updated regulations to ensure patient safety. Gilbert compares LLMs to an unpredictable genie, highlighting their potential and risks. He also explores the evolving responsibilities of physicians in a tech-driven landscape, advocating for collaboration to advance innovations while maintaining safety.
Stephen Gilbert emphasizes the need for tailored regulatory frameworks to evaluate AI technologies in healthcare, moving beyond traditional medical device regulations.
The unpredictable nature of large language model-based tools raises questions about their classification as medical devices and potential risks involved.
Gilbert advocates for a flexible regulatory approach that fosters innovation while ensuring patient safety in the rapidly evolving landscape of digital health and AI.
Deep dives
The Intersection of Veterinary Medicine and AI
The guest speaker, Stephen Gilbert, reflects on his journey from being a veterinary surgeon to a professor specializing in medical device regulatory science. He emphasizes that the skills required for diagnostic and treatment plans in veterinary medicine closely parallel those in human medicine. This background provided him with unique insights into the importance of bioinformatics and computational biology, which he pursued through advanced studies. His passion for these subjects ultimately led him to explore the intersection of digital health, AI, and regulatory science.
Challenges of Regulating AI in Medicine
The conversation highlights the need for tailored regulatory frameworks to evaluate the safety and efficacy of AI technologies, particularly large language models, in healthcare. Gilbert argues against applying traditional medical device regulations indiscriminately to new technologies, as they may not adequately address the unique challenges posed by AI systems. He emphasizes that current regulations evolved from historical devices and may not cater to the unpredictable nature of advanced AI solutions. This calls for an evolution in regulatory thinking to incorporate the dynamic capabilities of AI.
The Role of Large Language Models
Gilbert discusses the implications of using large language models in medicine, particularly their ability to generate responses across various contexts without being specifically trained for each scenario. He uses the metaphor of a genie to describe the unpredictable nature of these models, underscoring that once released, they cannot be confined. The complexities arise when attempting to determine if these tools should be classified as medical devices or if they pose inherent risks that warrant stricter scrutiny. He concludes that more research and open discussions are essential to understand these tools’ potential benefits and drawbacks.
Clinical Decision Support Tools and Their Regulation
The podcast addresses the current state of clinical decision support systems that utilize large language models, revealing that many such tools exist in the market, often under questionable regulatory classification. Gilbert asserts that these tools may not meet the necessary legal standards for medical devices, as their foundations may be based on inadequate clinical evidence. He suggests that a reevaluation of how these systems are marketed and approved is necessary for ensuring patient safety. The situation raises pressing questions about the responsibilities of healthcare providers who may be influenced by unregulated AI technology.
Navigating the Future of AI in Healthcare
Looking ahead, Gilbert stresses the importance of developing an adaptable regulatory framework that recognizes the evolving landscape of AI in medicine. He warns that rigid regulations could hinder innovation and lead to disparities in healthcare technologies globally. By embracing a more flexible and informed approach, regulators can better accommodate the nuances of AI applications in medicine. Ultimately, collaboration between industry stakeholders, regulators, and the medical community will be essential to navigate the complex interplay of technology and patient care.
Our guest is Prof. Stephen Gilbert (https://www.linkedin.com/in/stephen-gilbert-31ba2587/) who is a Professor of Medical Device Regulatory Science at the Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden where he teaches and conducts research on regulatory science with a team of colleagues. He is also News and Views Editor, Nature Portfolio – Digital Health. He worked in senior MedTech and Digital Heath roles in industry for 5 years, before returning to academia in 2022.
His research goals are to advance the regulatory science of software as a medical device and AI-enabled medical devices. Innovative digital approaches to healthcare must be accompanied by innovative approaches in regulation to ensure speed to market, to maximum access of patients to life saving treatments whilst ensuring safety on market. His main research interests are in: (i) data sharing and the European Health Data Space; (ii) approaches to market approval of adaptive AI enabled medical devices; (iii) drugdigital/AI-enabled medical device product realisation; (iv) digital/virtual twins: as an organising concept of the future of healthcare.”
Further Reading
Derraz B, Breda G, Kaempf C, Baenke F, Cotte F, Reiche K, Köhl U, Kather JN, Eskenazy D, Gilbert S. New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology. NPJ Precis Oncol [Internet]. Nature Publishing Group; 2024 Jan 30 [cited 2024 Jan 30];8(1):1–11. Available from: https://www.nature.com/articles/s41698-024-00517-w
Gilbert S, Harvey H, Melvin T, Vollebregt E, Wicks P. Large language model AI chatbots require approval as medical devices. Nat Med [Internet]. Nature Publishing Group; 2023 Jun 30 [cited 2023 Jun 30];1–3. Available from: https://www.nature.com/articles/s41591-023-02412-6
Gilbert S and Kather JN. Guardrails for the use of generalist AI in cancer care. Nature Reviews Cancer [Internet]. Nature Publishing Group; 2024 Apr 16 [cited 2024 Apr 16]. Available from: https://www.nature.com/articles/s41568-024-00685-8
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