Stanford Internal Medicine AI Curriculum — built by clinicians, refined by use.
In June 2025, a needs assessment of 34 Stanford IM residents found that nearly all had used at least one AI tool clinically, but mean self-rated AI knowledge was 2.6 of 5. Access without competence is a patient-safety risk.
This curriculum was built to close that gap with five hands-on sessions covering AI foundations, LLM mechanics, QI applications, vibe-coding for research, and red-teaming. The error-finding exercise on this site is the centerpiece of session 3 — LLMs for QI.
Four course directors led design, integration, and clinical framing. Each session was reviewed by practicing hospitalists before it reached residents — and rewritten after each cohort against what actually held up at the bedside.
You don't need Stanford's resources to run this. Three steps cover most programs — and the order matters less than starting at all.
Sources behind the needs assessment, the clinical guidelines used to score the exercise, and the broader literature this curriculum draws on.
Curriculum materials — slides, facilitator guides, H&P cases with answer keys, and the source code for this site — are available on request. We'd rather see this used at another program than sit in a Stanford folder.