Artificial Intelligence in Predicting Postoperative Nausea and Vomiting Among Surgical Patients Following General Anesthesia: A Systematic Review

Artificial intelligence (AI) may improve perioperative risk prediction by integrating complex clinical and physiological data, but its performance and readiness for clinical use remain uncertain. We evaluated AI models predicting postoperative nausea and vomiting (PONV), perioperative hypotension, prolonged intensive care unit (ICU) stay, in-hospital mortality, and neurological complications after general anesthesia. We searched bibliographic databases from inception to June 2026 for studies developing or validating AI-based prediction models in surgical patients undergoing general anesthesia. Two reviewers independently screened studies, extracted data, and assessed risk of bias and applicability using PROBAST+AI. Owing to heterogeneity in populations, outcomes, prediction horizons, algorithms, and validation methods, findings were synthesized narratively. Of 2,329 records identified, 22 studies were included. Sample sizes ranged from 221 to 106,860 participants. PONV models had area under the receiver operating characteristic curve (AUC) values of 0.561-0.960, whereas hypotension models ranged from 0.630 to 0.950. Models for prolonged ICU stay, mortality, and neurological complications showed AUCs up to 0.920, 0.930, and 0.890, respectively. Higher AUCs were not consistently accompanied by balanced sensitivity and specificity. All studies had high overall risk of bias, principally because of limited external validation, incomplete calibration reporting, and analytical concerns. AI shows promise for perioperative risk prediction, particularly for short-horizon hypotension and data-rich postoperative outcomes. However, substantial heterogeneity, high risk of bias, and scarce external validation preclude routine clinical implementation. Prospective, multicenter, calibrated, and impact-evaluated models are required.