Precision Anesthesia Through Real-Time Predictive Modeling

Abstract

Continuous intraoperative monitoring generates physiologic data at a density no human observer can fully exploit in real time, and a growing body of machine-learning work has sought to convert that data stream into actionable, minutes-ahead prediction of adverse intraoperative events. This review surveys four areas in which real-time predictive modeling has been applied to anesthetic care: prediction of intraoperative hypotension from arterial waveform and vital-sign features, closed-loop and reinforcement-learning-based control of hypnotic drug delivery, machine-learning-derived indices of nociception and anesthetic depth, and extension of intraoperative data streams into postoperative complication prediction such as acute kidney injury. Hypotension-prediction models built on waveform morphology and multicenter time-series data show strong retrospective discrimination, and nociception-level monitoring has the most direct randomized evidence of improved postoperative pain outcomes, while reinforcement-learning-based drug delivery remains largely confined to retrospective and simulated validation. Across all four domains, the central unresolved question is not whether these models can predict but whether acting on their output changes clinical outcomes, and we highlight generalizability across institutions, alert fatigue, and accountability for autonomous dosing decisions as the principal barriers to wider adoption. We conclude that precision anesthesia is a genuinely emerging paradigm rather than a settled one, with the clearest near-term priority being prospective, multicenter trials that test clinical impact rather than further retrospective model refinement.