Rare or unexpected postoperative neurosurgical complications pose a challenge due to clinical variability and gaps in available data. We introduce the Neurosurgical Uncertainty Index (NUI), an uncertainty-aware AI framework that integrates bootstrap sampling for aleatoric uncertainty, isolation forest anomaly detection, and clinical calibration to predict and stratify risks for 13 complications. NUI distinguishes between data-driven and model-driven uncertainty and highlights cases that conventional models often miss. In a cohort of 80 patients, the hybrid Rare Event Score (anomaly × uncertainty) achieved critical risk stratification with an AUROC of 0.92 (95% CI 0.85–0.97) for complications requiring intervention, demonstrating precision 89% for critical cases (Score < 0.8). Entropy thresholds (> 1.5 nats) flagged 18% of predictions for review, preventing three overconfidence errors. Interpretable risk tiers are designed to integrate seamlessly with clinical workflows. By merging machine learning, neurosurgery, and epistemology, NUI promotes AI that acknowledges its limitations, with the aim of safer surgery.

The authors have declared no competing interest.

This study did not receive any funding

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