Applications and Challenges of Artificial Intelligence in Neurosurgical Education in the Era of Medical-Engineering Integration

Abstract

Abstract: In the era of rapid advancements in medical-engineering integration, artificial intelligence (AI) is progressively permeating various aspects of medical education, offering unprecedented opportunities for innovation in neurosurgical training. Traditional neurosurgical education is constrained by a steep learning curve, limited practical resources, and challenges in implementing personalized teaching. The integration of AI provides new solutions to these challenges. This paper explores the key applications of AI in neurosurgical education, including virtual and augmented reality-based surgical simulation training, AI-assisted medical imaging analysis, and automated surgical operation assessment. These technologies enhance learning efficiency, optimize surgical training, and provide personalized real-time feedback to trainees, thereby improving overall teaching quality. However, the widespread implementation of AI in neurosurgical education still faces challenges related to data privacy protection, technological adaptability, and educator acceptance. Moving forward, it is essential to promote deeper integration between medicine and engineering, further optimize AI-driven educational models, and establish standardized teaching frameworks to ensure the sustainable development and broad application of AI in neurosurgical education.

Key words: artificial intelligence, neurosurgical education, medical-engineering integration

CLC Number:

R471

ZHANG Yang, CHENG Dan-ni. Applications and Challenges of Artificial Intelligence in Neurosurgical Education in the Era of Medical-Engineering Integration[J]. ZHONGHUA YANGSHENG BAOJIAN, 2025, 43(12): 100-103.

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