ArticleNPJ digital medicine2026
Artificial intelligence-enhanced microsurgical training: a systematic review.
Article in NPJ digital medicine, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.