PK/PD Foundations of Intravenous Anesthesia with Target-Controlled Infusion: From Hill’s Concentration–Effect Theory to the AI-Based Perspectives

Background: Pharmacokinetic/pharmacodynamic (PK/PD)-guided intravenous anesthesia and target-controlled infusion (TCI) have progressively transformed modern anesthetic practice from empirical drug administration toward individualized, model-informed precision anesthesia. Recent advances in neuromonitoring, closed-loop systems, and artificial intelligence (AI) are further expanding this paradigm. Methods: This Perspective article provides a conceptual and educational synthesis of the historical foundations, mathematical principles, clinical applications, and AI-based perspectives of PK/PD-guided intravenous anesthesia and TCI. Results: The concentration–effect relationship derived from Hill’s equation represents the conceptual basis of modern anesthetic pharmacology. Contemporary TCI systems integrate PK/PD models, effect-site targeting, synergistic drug interactions, and multimodal monitoring to improve anesthetic precision and safety. Recent evidence supports the advantages of total intravenous anesthesia (TIVA) in postoperative recovery outcomes, including reduced postoperative nausea and vomiting, emergence delirium, and improved quality of recovery. Emerging developments include increasingly generalizable PK “supermodels,” adaptive closed-loop control systems, multimodal AI integration, and patient-specific digital twins that may eventually support simulation of physiological responses and optimization of drug administration. However, biological variability, monitoring limitations, signal artifacts, model uncertainty, and the need for prospective validation, regulatory oversight, and continuous clinician supervision remain major challenges to the routine implementation of AI-assisted individualized anesthesia. Conclusions: PK/PD-guided anesthesia and TCI increasingly represent a clinically relevant framework for precision anesthesia, integrating pharmacology, monitoring, adaptive control, and AI-assisted systems. Future developments may progressively reduce the discrepancy between predicted and observed clinical effects, moving anesthetic practice toward continuously adaptive, feedback-driven, and individualized anesthesia care.