A Case-Based Framework for Teaching Artificial Intelligence Applications in Medical Education

Introduction: Artificial intelligence (AI) and machine learning (ML)-based tools are emerging across many areas of medicine. The next generation of medical professionals must be prepared to critically assess the tools they will use, including how they work and the ethical or legal implications of their implementation. To address this need, we developed a generalizable case-writing framework that enables educators and learners to engage with clinical cases integrating AI/ML concepts, clinical reasoning, and ethical considerations. Methods: We designed a case-writing template to compose clinical case scenarios that integrate clinical, technical, and ethical/legal learning objectives relating to AI applications in medicine. Medical students used this template to construct 3 cases. Student-authored cases were presented to a broader audience in 1-hour sessions: first as a pilot event, then as a class-wide lecture for 140 fourth-year medical students in a transition-into-residency course. Results: After the sessions, student audiences had a statistically significant increase in familiarity with key AI terminology, a greater appreciation for the importance of ethical considerations of AI applications in health care, and an increased perceived importance of integrating AI-related topics into the medical curriculum. Discussion: A case-based framework provides a scalable approach to integrating AI education into existing curricula by connecting clinical scenarios with technical concepts and the ethical considerations of AI adoption; cases offer a familiar format that helps learners understand AI applications in patient care while exploring implementation challenges. The case-writing template and 3 sample cases are provided as adaptable resources for educators seeking to incorporate AI literacy.