SynthesisNeurosurgical review2026
Applications of artificial intelligence in peripheral neurosurgery: a systematic review.
Synthesis in Neurosurgical review, 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is rapidly transforming surgical practice, including neurosurgery, plastic surgery, and general surgery. However, despite promising applications across surgical specialties, the scope of AI integration in peripheral nerve surgery remains unclear. This systematic review is registered and publicly available in PROSPERO (ID: 1061046) and aimed to explore, evaluate, and summarize current applications of AI in peripheral nerve surgery. Google Scholar, PubMed, Scopus, and IEEE Xplore were searched for peer-reviewed studies on AI in peripheral nerve surgery and related specialties. Eligible studies included original research using AI for imaging, diagnosis, treatment planning, or outcome prediction in this field. Non-English studies, abstracts, conference proceedings, and non-AI-related articles were excluded. Bias and quality were assessed using the Mixed Methods Appraisal Tool (MMAT 2018), A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR-2), Scale for the Assessment of Narrative Review Articles (SANRA), Joanna Briggs Institute (JBI) Critical Appraisal Checklist, and Prediction model Risk Of Bias ASsessment Tool (PROBAST). No meta-analysis was conducted, and findings were synthesized narratively with descriptive statistics where applicable. A total of 32 studies met inclusion criteria.Thirty-two studies published between 2009 and 2025 were included in this review. Most were retrospective or prospective human studies (28%). AI models demonstrated high performance (median accuracy 0.93, median sensitivity 0.96), with AI outperforming human experts (71.4%) in four head-to-head studies. Seven studies reported favorable clinical outcomes, including enhanced diagnostic accuracy and improved recovery trajectories. Quality assessments indicated generally high methodological rigor among included studies. This review explores AI’s potential to improve diagnostic precision and streamline workflows in peripheral nerve surgery. Limitations include high heterogeneity, absence of pooled data synthesis, and potential language and publication biases. Future research should focus on multi-center, prospective outcome-based studies and standardized reporting to guide clinical integration.
Indexed as
Identifiers
41882165What Socratic 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 Socratic graph.