Neurosurgery reimagined: How AI is redefining patient care and surgical excellence

Abstract

To review the latest advancements in artificial intelligence (AI) applications for neurosurgery, focusing on innovations in diagnostic imaging, intraoperative assistance, predictive analytics, and postoperative care, while addressing challenges and future directions for clinical integration. A comprehensive analysis of recent studies and technologies was conducted, including convolutional neural networks (CNNs), robotic-assisted systems, and AI-driven tools for imaging, surgical navigation, and predictive modeling. Key platforms such as Aidoc, Qure.ai, ROSA, and da Vinci were evaluated, alongside emerging approaches like federated learning and explainable AI (XAI). AI demonstrates transformative potential in neurosurgery, achieving up to 97.5% accuracy in tumor detection, 30% reduction in resection errors, and real-time molecular classification (e.g., DeepGlioma with >90% accuracy). Predictive analytics optimize personalized treatments, improving glioblastoma survival rates (24.3 vs. 17.5 months). AI-enhanced rehabilitation tools (e.g., VR/wearables) and simulation-based training further elevate outcomes. However, challenges persist, including data bias, algorithmic transparency, and ethical concerns. While AI significantly enhances precision, efficiency, and accessibility in neurosurgery, its "black box" nature and reliance on high-quality datasets limit widespread adoption. Collaborative efforts among clinicians, engineers, and policymakers are critical to address ethical, regulatory, and technical barriers. AI is redefining neurosurgical practice through innovations in diagnostics, robotics, and personalized medicine. Future success hinges on overcoming data and interpretability challenges while ensuring equitable, responsible implementation.

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