SynthesisFrontiers in artificial intelligence2026
Artificial intelligence applications in surgical education and training: a systematic review.
Synthesis in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.