Beyond the Black Box: Is Artificial Intelligence Ready to Reshape Neurosurgical Decision-Making? A Narrative Review.
Authors
Affiliations (3)
Affiliations (3)
- Department of Health Informatics, School of Engineering Kathmandu University Dhulikhel Nepal.
- Valley College of Technical Sciences Purbanchal University Biratnagar Nepal.
- Province Hospital, Karnali Province Surkhet Birendranagar Nepal.
Abstract
Artificial intelligence (AI) is increasingly being integrated into neurosurgical practice, offering capabilities in diagnostic imaging, surgical planning, and outcome prediction. However, the "black box" nature of many AI systems generating recommendations without a transparent rationale poses fundamental challenges to adoption in a specialty defined by high-stakes, irreversible interventions. This review critically examines whether AI is ready to reshape neurosurgical decision-making, synthesizing current evidence while systematically analyzing technical, ethical, and regulatory barriers to clinical integration. A systematic search of PubMed, Scopus, and Web of Science databases was conducted for peer-reviewed studies published between January 2020 and March 2026. Articles reporting AI applications in neurosurgical diagnosis, prognosis, or intraoperative guidance were included. Data were synthesized thematically across clinical domains, with a focus on model interpretability, validation status, and implementation barriers. AI demonstrates significant capabilities: diagnostic accuracy exceeding AUC 0.90 in tumor classification, prognostic improvements up to 15% over traditional methods, and 10%-20% complication reductions with AI-assisted planning. Currently, FDA-cleared tools enable automated tumor segmentation, aneurysm detection, and spinal navigation. However, critical gaps persist: external validation remains rare (< 20% of studies; e.g., 10 of 60 cerebrovascular studies (16.7%) reported external validation, with pooled AUC 0.84 [95% CI, 0.79-0.88] for thrombectomy outcome), most models are trained on homogeneous single-center datasets, and the "black box" problem limits clinician trust. From an implementation science perspective, human factors, including workflow integration and cognitive load, remain underexplored. AI is not ready for independent decision-making in neurosurgery but serves as a powerful augmentative tool when limitations are transparently addressed. Lessons from neurosurgery offer a blueprint for AI integration across high-stake medical specialties. The black box must be opened before AI can truly reshape clinical practice.