CYAIFeb 11

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

arXiv:2602.10527v11 citationsh-index: 2
Originality Synthesis-oriented
AI Analysis

This addresses the problem of outdated medical education for educators and students, but it is incremental as it builds on existing knowledge without new empirical results.

The paper tackles the lag in integrating AI into medical education by synthesizing literature to identify competencies and strategies, resulting in a framework for curriculum development to prepare physicians for AI-enhanced healthcare.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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