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Background <p>Artificial intelligence (AI) has transformed medical education through optimized instruction, competency assessment, and personalized learning. Its integration into neurosurgical education, given the field’s complexity and precision demands, warrants comprehensive exploration. To systematically evaluate AI applications in neurosurgical education, we conducted a scoping review adhering to PRISMA-ScR guidelines. Our Scopus search (up to May 2024) identified 23 eligible studies. Inclusion criteria encompassed peer-reviewed observational or experimental studies on AI in neurosurgical education. Narrative synthesis categorized findings into key domains.</p> Results <p>Four main key areas emerged: performance in board examinations, simulation-based training and tutoring, performance/skills/expertise analysis and assessment, and other applications. In board examinations, GPT-4 outperformed prior models and junior neurosurgeons in text-based questions but lagged in image-based tasks. Simulation training utilized neural networks to classify expertise and deliver individualized feedback, though rigid metrics risked oversimplifying skill progression. Machine learning algorithms assessed surgical performance, identifying metrics. Other innovations included AI-generated academic content, neuroanatomical segmentation, and instrument pattern analysis. Ethical concerns highlighted risks of overreliance, image-processing limitations, and the irreplaceable role of clinical intuition. Technical challenges included dataset biases and simulation realism.</p> Conclusions <p>AI enhances neurosurgical education through knowledge assessment, simulation feedback, and skill evaluation. However, integration requires addressing ethical dilemmas, improving multimodal data processing, and ensuring human-AI collaboration. Continuous model refinement, expanded datasets, and hybrid curricula combining AI analytics with expert mentorship are critical for safe, effective implementation. This evolution promises to elevate training quality while preserving the indispensable value of hands-on experience in neurosurgical practice.</p>

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Applications of artificial intelligence in neurosurgical education: a scoping review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月19日

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  • Hector Julio Piñera-Castro,
  • Christian Borges-García

摘要

Background

Artificial intelligence (AI) has transformed medical education through optimized instruction, competency assessment, and personalized learning. Its integration into neurosurgical education, given the field’s complexity and precision demands, warrants comprehensive exploration. To systematically evaluate AI applications in neurosurgical education, we conducted a scoping review adhering to PRISMA-ScR guidelines. Our Scopus search (up to May 2024) identified 23 eligible studies. Inclusion criteria encompassed peer-reviewed observational or experimental studies on AI in neurosurgical education. Narrative synthesis categorized findings into key domains.

Results

Four main key areas emerged: performance in board examinations, simulation-based training and tutoring, performance/skills/expertise analysis and assessment, and other applications. In board examinations, GPT-4 outperformed prior models and junior neurosurgeons in text-based questions but lagged in image-based tasks. Simulation training utilized neural networks to classify expertise and deliver individualized feedback, though rigid metrics risked oversimplifying skill progression. Machine learning algorithms assessed surgical performance, identifying metrics. Other innovations included AI-generated academic content, neuroanatomical segmentation, and instrument pattern analysis. Ethical concerns highlighted risks of overreliance, image-processing limitations, and the irreplaceable role of clinical intuition. Technical challenges included dataset biases and simulation realism.

Conclusions

AI enhances neurosurgical education through knowledge assessment, simulation feedback, and skill evaluation. However, integration requires addressing ethical dilemmas, improving multimodal data processing, and ensuring human-AI collaboration. Continuous model refinement, expanded datasets, and hybrid curricula combining AI analytics with expert mentorship are critical for safe, effective implementation. This evolution promises to elevate training quality while preserving the indispensable value of hands-on experience in neurosurgical practice.