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

TL;DR

This paper reviews current AI integration in medical education, emphasizing the need for structured, longitudinal curricula that combine technical and clinical skills to prepare future physicians for AI-enhanced healthcare.

Contribution

It proposes a comprehensive framework for developing AI curricula in medical education, emphasizing interdisciplinary, continuous, and balanced training approaches.

Findings

Effective AI education requires longitudinal integration throughout training.

Interdisciplinary collaboration enhances AI curriculum effectiveness.

Balanced focus on technical fundamentals and clinical applications is essential.

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.