Artificial Intelligence in Neurosurgical Education: A Systematic Review of Technical Skills Training, Clinical Reasoning, and Surgical Planning.
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 systemat...
| Publicado en: | Lokman Hekim Health Sciences Vol. 6; no. 2; pp. 303 - 314 |
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| Autores principales: | , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
KARE Publishing
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194639956&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194639956 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 27917835 N1MV jtl: Lokman Hekim Health Sciences issn: 27917835 maglogo: N pubinfo: dt: Jun2026 vid: 6 iid: 2 pid: 62027 pub: KARE Publishing artinfo: ui: 194639956 194639956 194639956 10.14744/lhhs.2026.20865 194639956 ppf: 303 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Artificial Intelligence in Neurosurgical Education: A Systematic Review of Technical Skills Training, Clinical Reasoning, and Surgical Planning. aug: au: Çahin, Ömer Selçuk Dinç, Samet affil: Department of Neurosurgery, Etlik City Hospital, Ankara, Türkiye sug: subj: Neurosurgery Education Artificial Intelligence Skill Acquisition Education Clinical Reasoning Strategic Planning Education Human Systematic Review PubMed Clinical Competence Sensitivity and Specificity Feedback Sample Size Study Design Decision Making, Clinical Machine Learning Support Vector Machine Decision Trees Random Forest Checklists ab: 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. 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. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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