Automated Anesthetic Drug Delivery Systems: Feasibility and Safety Assessment

Abstract

Closed-loop and target-controlled infusion technologies have advanced from single-drug proof-of-concept demonstrations to a growing body of randomized clinical evidence supporting automated titration of anesthetic drugs during general anesthesia. This review examines the control architectures underlying automated anesthetic drug delivery, from model-based target-controlled infusion through proportional-integral-derivative and model predictive control to emerging reinforcement-learning approaches, and synthesizes the clinical evidence for their feasibility and safety. Randomized trials across breast, thoracic, pediatric, and cardiac surgical populations indicate that closed-loop, bispectral-index-guided propofol delivery achieves tighter time-in-target control than manual titration without an apparent increase in adverse events, and multi-drug systems that jointly automate hypnosis, analgesia, and neuromuscular blockade have been demonstrated safely under close supervision. Formalized safety architectures incorporating pharmacokinetic-pharmacodynamic dosing constraints and fault detection are essential complements to control-algorithm performance, particularly given the vulnerability of processed-EEG feedback signals to intraoperative artifact. We conclude that fully autonomous anesthesia delivery remains distant, but a supervised, increasingly capable automated co-pilot model is both technically feasible now and the most realistic path toward broader clinical adoption, pending further multicenter validation and resolution of regulatory and liability questions.