Artificial Intelligence in Medical Education: Current Applications and a Proposed Comprehensive Integration Framework

Artificial intelligence (AI) is rapidly transforming healthcare, yet its integration into medical education remains inconsistent and lacks standardization across training levels. This comprehensive review synthesizes current research on AI applications within medical education, highlighting significant gaps between the growing clinical role of AI and its representation in undergraduate and postgraduate curricula. The review identifies disparities in AI awareness and utilization, with postgraduate trainees demonstrating greater familiarity than undergraduates, and notes that most existing educational efforts are concentrated in specialty training and continuing education, particularly in fields such as radiology, pathology, surgery, cardiology, and dentistry. While medical trainees generally express positive attitudes toward acquiring AI competencies, barriers such as the absence of standardized frameworks and AI taxonomy, limited faculty expertise, curricular constraints, and ethical considerations impede broader adoption. Drawing on examples of pioneering programs and a systematic analysis of curricular approaches, we propose a novel, tiered framework for comprehensive AI integration across the medical education continuum. This framework emphasizes universal AI literacy, critical evaluation skills, ethical awareness, and experiential learning at the undergraduate level, with extensions for specialty-specific training and advanced technical or leadership tracks. Recommendations include phased implementation strategies, faculty development initiatives, including “teach the teacher”, and competency-based assessment methods. The review concludes that adequate preparation of future physicians requires a shift from isolated AI initiatives to coordinated, longitudinal integration efforts supported by collaboration among educational institutions, professional societies, and technology experts. Future research should focus on evaluating educational outcomes, developing robust assessment tools for AI competencies, and examining the long-term clinical impact of AI training.