Abstract

Artificial intelligence (AI) and machine learning technologies are revolutionizing neuroanesthesia and neurocritical care through enhanced real-time monitoring, advanced neuroimaging analysis, and improved prognostic accuracy in patients with traumatic brain injury, stroke, intracranial hemorrhage, and neurosurgical conditions.1 This comprehensive review examines current AI applications including continuous physiological monitoring with predictive alerts, automated neuroimaging interpretation, outcome prognostication, personalized intraoperative management, and closed-loop anesthetic delivery systems. We address critical challenges including data quality, algorithmic bias, interpretability concerns, and regulatory barriers to clinical implementation. While AI demonstrates substantial promise in improving patient safety and outcomes in resource-constrained settings, successful integration requires rigorous validation, diverse population representation, clinician education, and establishment of clear governance frameworks. This review synthesizes evidence for Indian neuroanesthesiologists and neurocritical care specialists regarding AI implementation opportunities and necessary considerations for adoption.

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