Introduction: Artificial intelligence (AI) and machine learning (ML) are increasingly used in neurosurgical education to mitigate limitations of apprenticeship-based training (restricted operative exposure, duty-hour constraints) and to enable objective, scalable competency assessment. This systematic review synthesized and critically appraised evidence on AI/ML applications for technical skills training, clinical reasoning support, and surgical planning. Materials and Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020, we searched seven databases (SciSpace Deep Review, SciSpace Basic Search, SciSpace Full-Text Search, Web of Science Core Collection, PubMed, Google Scholar, and arXiv) for English-language, peer-reviewed studies published January 2010–January 2026. Two reviewers independently screened records, extracted data, and assessed risk of bias using design-appropriate appraisal tools. Given methodological heterogeneity, a narrative synthesis was conducted.
Results: From 789 records, 36 studies met the inclusion criteria. Most focused on technical skills training (69.4%), followed by surgical planning (27.8%); fewer evaluated clinical reasoning support. AI-based assessment systems differentiated expertise with 83–100% accuracy. AI-augmented tutoring and feedback systems yielded improvements comparable to expert instruction (effect sizes 0.20–0.66). Common limitations included small sample sizes, single-center designs, and limited external validation.
Discussion and Conclusion: AI/ML technologies demonstrate clinically meaningful benefits for neurosurgical technical skills training. Cognitive and decision-support applications remain less mature and require multi-institutional validation, standardized outcomes, and longitudinal evaluation to support broader curricular integration.