How to Teach Generative Artificial Intelligence in Undergraduate Medical Education

Abstract

Generative artificial intelligence (AI) refers to computational systems capable of analysing data, recognising patterns and generating outputs that may support decisions. In healthcare, AI has the potential to improve diagnostic accuracy and provide clinical decision support. As AI becomes ubiquitous in clinical workflows, clinical teachers must be prepared not only to use AI tools but also to interpret, appraise and oversee their outputs safely and effectively. However, evidence indicates that medical curricula have not kept pace with technological adoption; structured AI education remains sparse or inconsistent across institutions. To address this gap, educators must define what medical students should learn about AI and how to teach it. Whereas existing literature defines what learners should know about AI, our work provides a pragmatic framework for how they should learn to use it in practice. By integrating verification, critical appraisal and ethical reflection into everyday clinical teaching, our workflow offers a scalable and adaptable model for preparing future clinicians to engage safely and responsibly with generative AI.