Artificial intelligence (AI) is becoming a transformative force in neurosurgery, offering unprecedented capabilities for preoperative planning, intraoperative navigation, and decision support. With the continuously increasing precision required for brain surgery, AI-driven systems provide tools that enhance anatomical understanding, predict surgical outcomes, and improve the safety and accuracy of interventions. This article examines the current landscape, effectiveness, and emerging applications of AI in neurosurgical planning and navigation.

Recent advancements in machine learning, deep learning, and computer vision have enabled automated segmentation of brain tumors, extraction of anatomical structures, and identification of eloquent pathways through advanced neuroimaging interpretation. These tools reduce the manual burden on clinicians and increase consistency in planning complex procedures. Moreover, AI-based predictive modeling contributes to risk assessment and surgical strategy selection, improving patient-specific treatment planning. Robotic assistance and augmented reality (AR) integrated with AI further enhance intraoperative accuracy, enabling real-time adaptation to anatomical changes such as brain shift.

Despite the clear potential, challenges remain regarding data quality, algorithm transparency, generalizability, and ethical considerations. Many AI models rely on large datasets that may not reflect the full diversity of clinical presentations. Furthermore, while AI augments the neurosurgeon’s capabilities, it raises concerns about overreliance on automated systems and the need for robust validation in real clinical environments.

This article provides an in-depth analysis of current AI applications in neurosurgical planning, reviews the state of the art in intraoperative navigation technologies, discusses methodological considerations for integrating AI into clinical workflows, and outlines future directions, including autonomous surgical assistance, multimodal imaging fusion, and enhanced predictive analytics. The findings indicate that AI has the potential to significantly improve surgical precision, reduce operative risk, and personalize patient care, but successful implementation will require multidisciplinary collaboration, technical standardization, and ethical governance.

Artificial intelligence, neurosurgery, preoperative planning, navigation, deep learning, robotics, augmented reality, brain tumors, surgical precision, predictive modeling.

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