A new systematic review published in Cureus examines the literature to determine how well artificial intelligence (AI) predicts (PONV) among surgical patients following general anesthesia. The researchers found that while AI offers promise in this area, it isn’t quite ready to replace conventional assessments.
AI “may improve perioperative risk prediction by integrating complex clinical and physiological data, but its performance and readiness for clinical use remain uncertain,” write the researchers. “We evaluated AI models predicting postoperative nausea and vomiting (PONV), perioperative hypotension, prolonged [ICU] stay, in-hospital mortality, and neurological complications after general anesthesia.”
All told, 22 studies were included in the review, with sample sizes ranging from 221 to 106,860 participants. “AI models demonstrated potential for predicting PONV, perioperative hypotension, prolonged ICU stay, in-hospital mortality, and neurological complications following general anesthesia,” the researchers write. “The strongest performance was observed for short-horizon waveform-based hypotension prediction and selected procedure-specific or data-rich perioperative models. Nevertheless, performance varied markedly across populations and algorithms, and high discrimination did not consistently correspond to adequate sensitivity, specificity, calibration, or clinical utility.”
The researchers write, “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…AI should therefore be considered a promising adjunct to perioperative risk assessment rather than a clinically established replacement for conventional assessment.”
