What does this research mean for the field?

Medical AI education suffers from fragmentation, procedural bias, and affective domain under-representation, which can be addressed by the AI-PACE framework to longitudinally integrate AI competencies across medical training. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to address gaps in integrating AI education for generalist physicians by proposing a structured framework.

May 17, 2026Open Access

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

Key Points

The aim is to address gaps in integrating AI education for generalist physicians by proposing a structured framework.
Integrative review of 23 peer-reviewed articles published between 2016 and 2025.
Analysis focused on identifying structural gaps in AI education for medical professionals.
Proposed AI-PACE framework categorizes AI competencies based on Bloom’s Taxonomy.
Identified three major gaps: short-term interventions lacking reinforcement, procedural-field bias, and under-representation of the affective domain.
Proposed AI-PACE framework organizes AI competencies in a longitudinal manner across different education stages.
Framework aims to provide a structured approach to enhance AI integration in medical training.

Abstract

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.

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