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

npj Digital Medicine 2026 Open access

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Authors: Scott P. McGrath, Katherine K. Kim, Karnjit Johl, Haibo Wang, Nick Anderson
DOI: 10.1038/s41746-026-02768-2
Cited by: 6
References: 49

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.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Clinical Reasoning and Diagnostic Skills Explainable Artificial Intelligence (XAI)

Publication details

Journal / source
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Volume
9
Issue
1
Publication date
16 May 2026
Language
EN