Integrating Generative Artificial Intelligence Into Medical Education: Curriculum, Policy, and Governance Strategies

Abstract

The rapid advancement of generative artificial intelligence (GAI) is poised to revolutionize medical education, clinical decision-making, and health care workflow. Despite considerable interest and a surfeit of newly available tools, medical educators largely lack both competencies and guidance on how to incorporate the new and rapidly evolving world of GAI into the core medical school curriculum and experiences of undergraduate medical education. This Scholarly Perspective highlights the need for medical schools to adapt to this new paradigm by implementing policies, governance, and curricula that address the ethical, technical, and pedagogical implications of GAI. The authors recommend creating policies for appropriate GAI use, designed to protect institutional and patient data, and provide students with clarity on the appropriate use of AI for education. The authors suggest that implementing GAI governance at institutions is crucial to create guiding principles on ethical and equitable GAI use and involving students as coinventors of local innovation. The authors argue that providing faculty and learners with tools and training for safe experimentation with GAI and defining competencies for students and faculty are essential. Curricula for GAI should focus on implications of clinical uses. The authors propose a set of new competencies for GAI that build on those already established for AI in general. Given how dynamic the world of GAI is and how quickly new innovations are changing longstanding practices of clinical medicine, it is imperative that the medical education community acts together to share best practices, gather data to assess the impact of GAI education, continuously update the expected competencies of medical students, and help students prepare for a career that will be continually changed by GAI.

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