Artificial intelligence across the surgical learning continuum: Current applications, educational challenges and future directions

Artificial intelligence (AI) is transforming surgical education through adaptive learning, simulation, computer vision, learning analytics, and decision support. This structured narrative review synthesizes AI applications across the surgical learning continuum, from undergraduate education and clinical clerkship through residency, fellowship, independent practice, and continuing professional development. A structured literature search of PubMed/MEDLINE, Scopus, and Google Scholar was conducted between May and June 2026, with evidence organized using the AI-assisted Surgical Learning Continuum. AI can support personalized learning, clinical reasoning, simulation, objective technical assessment, operative video analysis, automated feedback, performance analytics, and longitudinal competency tracking. Generative and multimodal AI may further enable interactive and adaptive learning. However, implementation is constrained by algorithmic bias, hallucinations and reliability concerns, data privacy, explainability, automation bias, faculty preparedness, and regulatory uncertainty. The evidence remains heterogeneous, and technical performance does not necessarily establish educational effectiveness or clinical reliability. AI should therefore augment rather than replace human educators, preserving mentorship, professional judgment, ethical reasoning, accountability, and patient-centered care. Future research should prioritize rigorous validation, meaningful educational outcomes, cost-effectiveness, and responsible implementation across diverse surgical training environments.