What question did this study set out to answer?

This research aims to explore the impact of artificial intelligence on neurosurgical training and education.

July 2, 2026

Artificial Intelligence in Neurosurgical Training: Applications, Challenges, and Future Directions

SSS SankarRoyal College of Surgeons in Ireland KRKhyber Afridi RabbiRoyal College of Surgeons in Ireland RCRosin F. CrosbyRoyal College of Surgeons in Ireland

Key Points

This research aims to explore the impact of artificial intelligence on neurosurgical training and education.
Structured literature search conducted on PubMed and SpringerOpen for AI applications in neurosurgical education from 2020 to 2025.
Twenty-one studies were analyzed assessing AI's role in skill assessment and feedback systems for trainees.
Data synthesized based on AI modalities and pedagogical functions with a focus on measurable outcomes.
Machine learning and deep learning tools showed effective skill assessment using motion tracking and video analytics.
Extended-reality platforms integrated with AI enhanced skill acquisition and retention among trainees.
Identified barriers include dataset bias, ethical challenges, and the need for robust validation of AI systems.

Abstract

Abstract Artificial intelligence (AI) has rapidly transformed neurosurgical practice, with emerging applications in education and training. Traditional apprenticeship-based learning is constrained by limited operative exposure, subjective evaluation, and variability in teaching quality. AI-driven tools—including machine learning (ML), deep learning (DL), extended-reality simulations, and intelligent tutoring systems—offer data-driven, scalable, and personalized learning opportunities for neurosurgical trainees. A structured literature search was conducted using PubMed, PubMed Central, and SpringerOpen for peer-reviewed articles published between 2020 and 2025. Studies were included if they examined AI applications in neurosurgical education involving residents or trainees, reported measurable educational outcomes, or assessed AI-mediated feedback systems. Articles focusing on post-training surgeons or non-neurosurgical specialties were excluded. Data were synthesized narratively according to AI modality and pedagogical function. Twenty-one eligible studies were identified. ML and DL systems demonstrated accurate skill assessment via motion tracking, force sensors, and video analytics. Generative models and large-language-model-based tutors (e.g., ChatGPT) showed potential for knowledge assessment and content generation. Extended-reality platforms combined with AI feedback improved technical skill acquisition and retention. However, methodological diversity, dataset bias, limited external validation, and ethical issues—such as transparency, consent, and unequal access—remain major barriers. AI provides transformative avenues for neurosurgical education by enabling objective assessment and immersive simulation. Nonetheless, robust validation, explainable AI models, and global equity in resource distribution are essential before widespread implementation. Future research should focus on integrating standardized, ethically governed AI frameworks into surgical curricula.

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