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AI-PACE: A Framework for Integrating AI into Medical Education

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

TL;DR

This paper tackles the gap between rapid AI integration in clinical practice and medical education by proposing the AI-PACE framework, which structures AI competencies into Psychomotor, Affective, Cognitive domains, plus an Embedded longitudinal pillar. Through a systematic literature review and thematic analysis, it identifies gaps such as fragmented training, specialty-bias, and neglect of affective skills, then prescribes a spiral, longitudinal curriculum that begins in undergraduate medical education and extends through CME. The model emphasizes integration over additive approaches, interdisciplinary collaboration, and ongoing faculty development to sustain relevance amid fast AI evolution. Implementing AI-PACE aims to produce clinicians who can critically appraise AI tools, maintain patient-centered care, and lead responsible AI integration in practice. These contributions provide a foundation for standardized, cross-institutional AI education with attention to ethical, legal, and workflow considerations, ultimately improving safety and effectiveness of AI-enabled healthcare.

Abstract

The integration of artificial intelligence (AI) into healthcare is accelerating, yet medical education has not kept pace with these technological advancements. This paper synthesizes current knowledge on AI in medical education through a comprehensive analysis of the literature, identifying key competencies, curricular approaches, and implementation strategies. The aim is highlighting the critical need for structured AI education across the medical learning continuum and offer a framework for curriculum development. The findings presented suggest that effective AI education requires longitudinal integration throughout medical training, interdisciplinary collaboration, and balanced attention to both technical fundamentals and clinical applications. This paper serves as a foundation for medical educators seeking to prepare future physicians for an AI-enhanced healthcare environment.

AI-PACE: A Framework for Integrating AI into Medical Education

TL;DR

This paper tackles the gap between rapid AI integration in clinical practice and medical education by proposing the AI-PACE framework, which structures AI competencies into Psychomotor, Affective, Cognitive domains, plus an Embedded longitudinal pillar. Through a systematic literature review and thematic analysis, it identifies gaps such as fragmented training, specialty-bias, and neglect of affective skills, then prescribes a spiral, longitudinal curriculum that begins in undergraduate medical education and extends through CME. The model emphasizes integration over additive approaches, interdisciplinary collaboration, and ongoing faculty development to sustain relevance amid fast AI evolution. Implementing AI-PACE aims to produce clinicians who can critically appraise AI tools, maintain patient-centered care, and lead responsible AI integration in practice. These contributions provide a foundation for standardized, cross-institutional AI education with attention to ethical, legal, and workflow considerations, ultimately improving safety and effectiveness of AI-enabled healthcare.

Abstract

The integration of artificial intelligence (AI) into healthcare is accelerating, yet medical education has not kept pace with these technological advancements. This paper synthesizes current knowledge on AI in medical education through a comprehensive analysis of the literature, identifying key competencies, curricular approaches, and implementation strategies. The aim is highlighting the critical need for structured AI education across the medical learning continuum and offer a framework for curriculum development. The findings presented suggest that effective AI education requires longitudinal integration throughout medical training, interdisciplinary collaboration, and balanced attention to both technical fundamentals and clinical applications. This paper serves as a foundation for medical educators seeking to prepare future physicians for an AI-enhanced healthcare environment.
Paper Structure (20 sections, 2 figures, 2 tables)

This paper contains 20 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Selection Criteria
  4. Analytical Approach
  5. Current State of AI Education
  6. Fragmented Implementation and Knowledge Decay
  7. Specialty Imbalance and the Generalist Gap
  8. The "Human" Gap: Affective Competencies
  9. The AI-PACE Framework
  10. Psychomotor Domain (Skills & Workflow)
  11. Affective Domain (Appropriate Entrustment & Trust Calibration)
  12. Cognitive Domain (Knowledge)
  13. Embedded (Longitudinal Integration)
  14. Integrated Pedagogical Methods:
  15. Implementation Strategy
  16. ...and 5 more sections

Figures (2)

  • Figure 1: AI-PACE Taxonomic Framework for Medical AI Competencies within Bloom’s Taxonomy Domains.
  • Figure 2: A proposed approach to implementing an AI curriculum into an existing program.