AI-PACE: a framework for integrating AI into medical education

2026/02/11 by Scott P. McGrath, Katherine K. Kim, Karnjit Johl +2 · 1 voice
Computer Science · Medicine · #Artificial Intelligence in Healthcare and Education #Clinical Reasoning and Diagnostic Skills #Explainable Artificial Intelligence (XAI) #cs.AI #cs.CY

paper · pdf · doi:10.1038/s41746-026-02768-2

openalex created_date 2026/05/16 · openalex publication_date 2026/05/16 · openalex updated_date 2026/08/01

Abstract

Medical AI education remains fragmented, specialty-skewed, and lacks longitudinal structure, particularly for generalist physicians. Through an integrative review of 23 peer-reviewed articles (2016-2025), we identified three structural gaps: short-term interventions without reinforcement, procedural-field bias, and consistent under-representation of the Affective domain. We present AI-PACE (Psychomotor, Affective, Cognitive, Embedded), a Bloom's Taxonomy-grounded framework organizing AI competencies longitudinally across undergraduate, graduate, and continuing medical education.

Citations

• The integrative review: updated methodology

Discussions

• 🧪Thrilled to share our new preprint! Medical education is failing to keep up with AI in healthcare, so we developed the AI-PACE framework to build AI literacy from medical school through clinical pra [bsky, 7 points, 0 comments]

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