A Systematic Literature Review of Precision Anesthesia Through Machine Learning: Automated Drug Titration and...

The integration of machine learning (ML) and artificial intelligence (AI) technologies into anesthesia practice represents a paradigm shift toward precision medicine by enabling automated, data-driven decision-making during surgery. This systematic review aimed to evaluate current applications of ML for automated drug titration and real-time physiologic optimization in anesthesia. A comprehensive literature search, adhering to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, was performed across five databases (SciSpace, Google Scholar, PubMed, ArXiv, and Scopus) for studies published between 2010 and 2025. Eligible studies examined ML- or AI-based systems for closed-loop anesthesia control, individualized dosing, or physiologic monitoring in either clinical environments or validated simulation settings. Of the 245 studies initially identified, 26 met the inclusion criteria after title and abstract screening. The most commonly used ML architectures included reinforcement learning (RL), convolutional neural networks (CNNs), and ensemble tree-based methods. Key applications included closed-loop propofol infusion control, multimodal signal integration for depth-of-anesthesia assessment, and adaptive physiologic optimization systems. ML-guided controllers demonstrated superior performance in target maintenance, dosing precision, and physiologic stability compared to conventional proportional-integral-derivative (PID) algorithms. Despite these promising findings, most studies relied on retrospective data or simulation-based testing, with only a small number advancing to prospective clinical evaluation. While ML-enabled precision anesthesia shows substantial promise for improving dosing accuracy, patient safety, and intraoperative efficiency, broad adoption will depend on rigorous clinical validation, clear regulatory pathways, and strong safety frameworks to ensure dependable performance in real-world settings.