Development of AI competencies within the medical curriculum
Abstract
Introduction: The rapid integration of artificial intelligence (AI) into healthcare is transforming clinical practice and redefining the competencies required of future physicians. As AI increasingly supports diagnostics, clinical decision-making, and healthcare management, medical curricula must evolve to ensure graduates possess the knowledge, skills, and ethical competencies necessary to interact effectively with AI-enabled systems.
Methodology: A systematic review was conducted following PRISMA guidelines. Literature searches were performed in PubMed, Scopus, and IEEE Xplore for studies published between 2020 and 2025. Eligibility criteria focused on original studies addressing AI competency development, curricular interventions, educational frameworks, and AI-related training within medical education. Following screening and eligibility assessment, 20 studies were included in the final synthesis.
Results: AI literacy was the most frequently identified competency, alongside clinical AI applications, data science, ethical reasoning, critical appraisal, and human-AI collaboration. Integrated and longitudinal curriculum models emerged as the predominant approaches for competency development. Active learning strategies, particularly simulations, workshops, project-based learning, and authentic clinical applications, were consistently associated with positive educational outcomes.
Discussion: The evidence indicates a transition from isolated AI educational initiatives toward…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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