From compliance to competence: a multi-level model for AI governance in undergraduate medical education
A model for AI governance in undergraduate medical education has been proposed, framing the goal as moving students beyond rule-following toward genuine competence in using and overseeing AI tools in clinical contexts.
Why this matters: Medical students are going to graduate into hospitals full of AI systems making recommendations about diagnosis, treatment, and triage. Teaching them to tick compliance boxes is not enough. They need to understand how these tools fail, when to push back, and who is responsible when an AI-assisted decision goes wrong. That is not a technical question. It is a professional accountability question, and medical schools are just starting to take it seriously.
Who should care: AI governance · Lawyers · Administrators · Compliance · General readers · Policy
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