Work

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

Year: 2026

Type: article

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.

Source: npj Digital Medicine

DOI: 10.1038/s41746-026-02768-2

Authors: Scott P. McGrath, Katherine K. Kim, Karnjit Johl, Haibo Wang, Nick Anderson

Institutions: University of California, Berkeley, University of California, Davis, Sun Yat-sen University, The First Affiliated Hospital, Sun Yat-sen University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Quality education, Good health and well-being

Open Access status: gold

Estimated APC paid: $4,090