The role of artificial intelligence in diagnostic neurosurgery: a systematic review
Abstract
Artificial intelligence (AI) is increasingly applied in diagnostic neurosurgery, enhancing precision and decision-making in neuro-oncology, vascular, functional, and spinal subspecialties. Despite its potential, variability in outcomes necessitates a systematic review of its performance and applicability. A comprehensive search of PubMed, Cochrane Library, Embase, CNKI, and ClinicalTrials.gov was conducted from January 2020 to January 2025. Inclusion criteria comprised studies utilizing AI for diagnostic neurosurgery, reporting quantitative performance metrics. Studies were excluded if they focused on non-human subjects, lacked clear performance metrics, or if they did not directly relate to AI applications in diagnostic neurosurgery. Risk of bias was assessed using the PROBAST tool. This study is registered on PROSPERO, number CRD42025631040 on January 26th, 2025. Within the 193 studies, neural networks (30%) and hybrid models (48.2%) dominated. Studies were categorised into neuro-oncology (52.69%), vascular neurosurgery (19.89%), functional neurosurgery (16.67%), and spinal neurosurgery (11.83%). Median accuracies exceeded 85% in most categories, with neuro-oncology achieving high diagnostic accuracy for tumour detection, grading, and segmentation. Vascular neurosurgery models excelled in stroke and intracranial haemorrhage detection, with median AUC values of 88% and 97%, respectively. Functional and spinal applications showed promising results, though variability in…
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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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