Critical appraisal of Artificial Intelligence and deep-learning tools for intraoperative neurosurgery: hype versus evidence

Abstract

Artificial intelligence (AI) is beginning to aid in several components of the intraoperative workflow, including navigation, instrument tracking, ultrasound analysis, video segmentation, and MRI reconstruction. Early studies demonstrate technical promise but are based on small, single-center datasets with limited heterogeneity and generalizability, and often lead to model overfitting. External validation is rare, and few trials measure the impact on decision-making, complications, or the extent of resection. Differences in annotation standards, imaging protocols and reporting also contribute to the slow speed of translation. Physical, ethical, and regulatory barriers add complexity, especially in settings with limited resources. Recent FDA, EU, and WHO guidance emphasizes lifecycle monitoring, transparency, and real-world evidence, raising the bar for clinical adoption. Progress will require shared databases, standardized reporting, and multicenter implementation studies that track workflow and patient outcomes. Intraoperative AI will definitely not replace a surgeon's judgment, but if carefully developed and rigorously tested, it may provide meaningful clinical value.

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