Artificial Intelligence in Medical Education: Curriculum Design, Assessment Models, and Educational Infrastructure Across Undergraduate and Residency Training—A Narrative Review

Artificial intelligence (AI) is rapidly becoming an integral part of everyday clinical practice, including cardiology and cardiovascular surgery. As AI increasingly influences diagnostic and therapeutic deci-sions, physicians are expected to engage with these systems in a critical, safe, and ethically grounded manner. This narrative review aims to explore how AI can be systematically integrated into undergra-duate and residency medical education, with particular focus on curriculum design, teaching strategies, assessment models, and educational infrastructure, while considering the context of the Turkish medi-cal education system. A narrative synthesis of international medical education literature, policy docu-ments, and institutional reports was conducted without quantitative meta-analysis. The review was guided by the principles of human-in-the-loop clinical reasoning, ethical AI use, and patient safety. Effective integration of AI into medical education requires a longitudinal and staged curriculum span-ning preclinical, clinical, and residency training. Assessment strategies must explicitly address AI-assisted decision-making and be supported by transparent institutional policies governing AI use in examinations, along with a secure, regulation-compliant digital infrastructure. Educational approaches should encourage learners to critically appraise and contextualize AI outputs rather than accept them uncritically. The reviewed literature supports a competency-based educational framework that integra-tes AI literacy, ethical reasoning, and context-aware clinical judgment. AI education should be consi-dered a core clinical competency that strengthens rather than replaces human judgment. Particularly in high-risk cardiovascular disciplines, a standardized, ethics-centered, and competency-based educatio-nal framework is essential to prepare future physicians for AI-augmented healthcare environments.

Keywords: Artificial intelligence, assessment, clinical decision support, ethics, medical education, residency trai-ning