ARTIFICIAL INTELLIGENCE IN ANESTHETIC DRUG DELIVERY AND HEMODYNAMIC CONTROL: CLOSED-LOOP SYSTEMS, PREDICTION ALGORITHMS, AND A PRACTICAL IMPLEMENTATION APPROACH

Abstract

Modern anesthesia is a high-frequency control problem: clinicians must continuously titrate hypnotics, opioids, fluids, and vasopressors to achieve adequate hypnosis/analgesia while avoiding hemodynamic instability and downstream organ injury. Artificial intelligence (AI), machine learning, and classical control engineering are increasingly embedded in perioperative monitors and drug delivery platforms, enabling decision support and closed-loop control. Randomized trials and meta-analyses indicate that closed-loop systems for hypnosis (typically processed EEG targets such as BIS) improve time-in-target and reduce overshoot/undershoot compared with manual titration. Multi-variable systems that co-manage hypnosis, analgesia, and fluids are feasible and may improve short-term recovery outcomes in selected settings. On the hemodynamic side, intraoperative hypotension is common and associated with myocardial and kidney injury, AI-based early warning systems using arterial waveforms can predict hypotension minutes before onset and, when paired with treatment protocols, may reduce hypotension burden in some trials. Closed-loop vasopressor and fluid systems improve protocol adherence and reduce hypotension in perioperative and early postoperative care.  AI-enabled decision support and closed-loop controllers can improve stability of anesthesia and blood pressure management, but they should be implemented as supervised systems with clear safety constraints, manual override, and ongoing performance monitoring. Future multicenter trials should prioritize patient-centered outcomes, external validation, and transparent reporting.