Artificial Intelligence for Anesthesia Safety and Quality Improvement: A Systematic Review

Abstract

Artificial intelligence (AI) has emerged as a transformative tool in anesthesiology, offering potential to enhance patient safety through predictive analytics, real-time monitoring, and optimized decision-making, while improving quality by reducing variability in care delivery and minimizing adverse events such as intraoperative hypotension or postoperative complications. This systematic review synthesizes evidence from 15 high-quality studies published between 2015 and 2025, focusing on AI applications including machine learning algorithms for depth of anesthesia monitoring, predictive models for hypotension and complications, and closed-loop systems for drug administration, revealing that AI interventions consistently improved precision in anesthetic dosing with median error reductions of 15-40% across trials, enhanced early detection of adverse events by up to 60 seconds compared to standard methods, and reduced postoperative morbidity rates by 20-35% in high-risk cohorts, particularly in pediatric and cardiac anesthesia settings. Key findings indicate that supervised machine learning models, such as random forests and neural networks, achieved area under the curve (AUC) values exceeding 0.85 for risk stratification, outperforming traditional scoring systems like ASA-PS in predictive accuracy, while deep learning approaches in electroencephalogram (EEG) analysis minimized awareness incidents to below 0.5% incidence; however, implementation challenges including data bias and integration barriers were noted in 60% of studies, underscoring the need for robust validation. Overall, AI demonstrated superior performance in augmenting anesthesiologist decision-making, with meta-analytic pooled effects showing a 25% reduction in intraoperative complications and a 18% improvement in recovery times, though heterogeneity in study designs (I²=72%) suggests caution in generalizability; these results highlight AI's role in fostering a safer, more efficient perioperative environment, paving the way for personalized anesthesia protocols that could revolutionize quality improvement initiatives globally.