Applications of Artificial Intelligence in Neurosurgical Education: A Scoping Review
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Background
Artificial intelligence (AI) has revolutionized medical education due to its capacity to optimize instruction, assess competencies, and tailor learning. Given the interdisciplinary nature and transformative potential of AI, its integration into neurosurgical education merits thorough examination.
Objective
To evaluate systematically the applications of AI in neurosurgical education.
Methods
A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Original observational and experimental studies published in peer-reviewed journals that investigated the use of AI in neurosurgical education were included. The search was carried out in Scopus up to May 2024, and studies not written in English or Spanish were excluded. The selection process and data extraction were performed independently by two reviewers. A narrative synthesis approach was employed.
Results
Twenty-three studies were selected, identifying four main areas. In board examination performance, advanced models like ChatGPT-4 outperformed previous versions and residents in answering neurosurgical exam questions, achieving higher accuracy in complex theoretical and image-based inquiries. In simulation training and tutoring, neural networks analyzed surgical simulation data, classifying participants by expertise levels and providing individualized, metric-based feedback that improved technical skills and identified distinct learning patterns among trainees. In performance evaluation, machine learning techniques assessed surgical skills during simulations with high accuracy, identifying key behavioral metrics associated with expertise. Other innovative applications included AI for neuroanatomical segmentation in imaging studies, analysis of surgical instrument utilization patterns through computer vision, and generation of academic content. These studies highlighted AI’s multifaceted roles and ethical considerations, such as overreliance risks and the need for robust databases and realistic simulations.
Conclusions
AI has enhanced neurosurgical education by improving knowledge assessment, simulation training, and performance evaluation. Despite its transformative potential, ethical and technical challenges persist. The continuous development of AI models and their responsible implementation are recommended to further optimize neurosurgical training and ensure safe integration into educational curricula.
