Clinical Translation of Artificial Intelligence in Anesthesiology: Application Advances, Evidence Challenges, and a Governance Framework

Abstract

Artificial intelligence (AI) is being progressively applied across multiple domains of perioperative anesthesia management, including preoperative risk assessment, intraoperative physiological monitoring, depth-of-anesthesia evaluation, ultrasound-guided regional anesthesia, closed-loop drug delivery, and postoperative complication prediction. Advances in machine learning (ML), deep learning (DL), and generative artificial intelligence (GenAI) have driven the rapid growth of intelligent research in anesthesiology. However, improved algorithmic performance does not equate to clinical benefit, and a substantial evidence gap remains between model development and improvement in patient outcomes. Using a system-informed narrative review approach, this article comprehensively analyzes the clinical application, evidence maturity, translation barriers, and ethical governance issues of AI in anesthesiology. Available evidence indicates that AI has accumulated a relatively substantial research base in perioperative risk prediction, intraoperative hypotension prediction, and selected monitoring support; yet most studies remain at the stage of model development and internal validation, lacking multicenter, prospective studies and clinical validation with patient hard outcomes as endpoints. Based on existing evidence, we propose a clinical translation maturity framework for anesthesiology AI, evaluating the progression of AI tools from algorithmic research to clinical application across five dimensions—technical, validation, clinical, implementation, and governance maturity—and discuss key ethical and governance issues, including algorithmic bias, insufficient interpretability, data security, responsibility attribution, and physician trust. Future development of anesthesiology AI should not focus solely on model predictive performance, but should shift toward a patient-benefit-centered clinical evaluation system, promoting the safe, equitable, and sustainable integration of AI into perioperative practice through high-quality prospective research, standardized reporting norms, and physician-led human–machine collaboration.