Abstract
Artificial intelligence (AI) offers objective, adaptive tools for skill enhancement in microsurgical training, but evidence is fragmented. This systematic review evaluates AI-enhanced training efficacy compared to traditional methods, focusing on technical performance, learning efficiency, and skill retention. Following PRISMA guidelines, databases (MEDLINE, Embase, Cochrane, IEEE Xplore, Web of Science) were searched from January 2010. Data on study characteristics, AI models, outcomes (time, errors, skill metrics), risk of bias, evidence certainty (GRADE), methodological quality, and reporting quality were extracted and synthesized narratively. From 2,056 records, 13 studies were included, involving 3–50 participants, mostly single-centre with varied designs. AI/ML models, such as Mask R-CNN, YOLOv2, ResNet-50, and other convolutional neural networks, were primarily used for assessment or guidance/coaching, focusing on instrument tracking (30.8%), motion analysis (23.1%), tutoring/guidance (15.4% each). Median accuracy 83.8% (IQR 78.4–88.2%). AI improved technical skills (reduced errors) and learning curves via real-time feedback, with promising retention outcomes. RoB high; evidence certainty very low. Reporting quality high/moderate, external validation poor. AI enhances microsurgical training with objective metrics and personalised feedback, showing promising technical advantages in simulations. However, heterogeneous, low-quality evidence limits generalisability. Future research needs multi-centre RCTs, standardised outcomes, external validation, and ethical considerations for clinical translation.
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Data availability
The datasets generated and/or analyzed during the current study (including template data collection forms and data extracted from included studies) are not publicly available due to not being deposited in a public repository, but are available from the corresponding author on reasonable request.
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Both Wameth Alaa Jamel and Mohammed Jameel share the first authorship for the project.
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W.A.J. and M.J. contributed equally to this work and share first authorship. W.A.J. performed study screening, data extraction, draft writing, and creation of all tables. M.J. performed data extraction, draft writing, AMSTAR-2 appraisal, and creation of all figures. I.R. contributed to draft writing and conducted risk of bias assessments. Y.F.Y. designed and executed the database searches and assisted with data extraction and risk of bias assessments. R.P. performed study screening and contributed to GRADE certainty of evidence evaluation. V.d.l.T. contributed to GRADE certainty of evidence evaluation. I.S. developed the concept and design of the study, provided supervision and critical guidance throughout, and critically reviewed and revised the manuscript.
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Jamel, W.A., Jameel, M., Riaz, I. et al. Artificial intelligence–enhanced microsurgical training: a systematic review. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02452-5
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DOI: https://doi.org/10.1038/s41746-026-02452-5
