Artificial intelligence applications in surgical education and training: a systematic review
Abstract
Introduction: Surgical training is shifting toward scalable, data-driven education as operative complexity and patient safety expectations increase. AI can support objective feedback and cognitive guidance.
Methodology: We conducted a PRISMA-guided systematic review using a PICO framework and searched PubMed, Scopus, and IEEE Xplore for peer-reviewed studies published between 2020 and 2025.
Results: Of 1,109 records, 21 studies met the inclusion criteria. Included studies covered simulation-based, robotic, laparoscopic, and computer-assisted training using deep learning/computer vision, tutoring or predictive models, and language-based tools. Performance outcomes predominated (81.0%) over engagement-related outcomes (19.0%), and reported effects were mainly positive (76.2%) or increased (23.8%).
Discussion: Evidence suggests near-term, task-specific gains when AI provides objective measurement and feedback, but comparability is limited by heterogeneous endpoints, small samples, and single-center designs.
Conclusion: AI-enabled surgical education shows promise for objective assessment and adaptive instruction, but multicenter longitudinal studies with standardized metrics are still needed.
How strong is the evidence?
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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