Artificial intelligence in predicting anesthetic complications: current techniques, clinical applications, and limitations.

Artificial intelligence (AI) is revolutionizing anesthesiology by enhancing the prediction and management of perioperative complications, including intraoperative hypotension, respiratory failure, postoperative nausea and vomiting (PONV), and pain control challenges. This scoping review synthesizes evidence from 82 studies, identified through a systematic search of PubMed, Scopus, Web of Science, and grey literature from January 2010 to September 2025, to map AI techniques, their clinical applications, and limitations. Techniques include Machine Learning (ML) (e.g., random forests, support vector machines), deep learning, natural language processing (NLP), Computer Vision, Bayesian models, and fuzzy logic, applied across preoperative, intraoperative, and postoperative phases. AI models achieve superior predictive accuracy (AUC 0.85-0.94) compared to traditional methods (AUC 0.76-0.88), enabling early detection of complications and reducing opioid use by 15-35%. Applications include preoperative risk stratification, intraoperative monitoring, and postoperative analgesia optimization. Challenges include algorithmic bias, data reliability, interoperability, and real-time integration barriers. Ethical considerations emphasize transparency, equity, and clinician oversight. This review positions AI as a decision-support tool within the P4 medicine framework (Predictive, Preventive, Personalized, Participatory), advocating for validation, ethical frameworks, and integration with anesthesia information management systems (AIMS) to enhance perioperative safety.