AI-Pace: A Framework For Integrating AI Into Medical Education

Scott McGrath, University of California, Berkeley

Karnjit Johl, University of California, Davis

Katherine K. Kim, University of California, Davis

Haibo Wang, Sun Yat-sen University

Nick Anderson, University of California, Davis

Date

02/2026

AI Overview

Why it matters

Medical AI education remains fragmented, specialty-skewed, and lacks longitudinal structure, particularly for generalist physicians. Clinicians increasingly face a shadow health system of AI tools without formal training to evaluate, verify, or oversee them.

Study at a glance

Question: How can AI competencies be structured longitudinally across undergraduate, graduate, and continuing medical education?
Setting and data: United States medical training programs, 23 peer-reviewed articles published between 2016 and 2025.
Method: Integrative literature review and thematic analysis.

Key findings

Existing medical AI education is heavily skewed toward procedural and image-centric specialties, leaving a gap in training for generalist physicians.
Current educational efforts largely rely on isolated, short-term workshops or electives, which risk knowledge decay without ongoing reinforcement.
Published frameworks consistently focus on knowledge and technical skills while under-addressing affective competencies, such as calibrating trust, recognizing automation bias, and preserving empathy.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

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.