Artificial Intelligence for Predicting Perioperative Outcomes in Cardiac Surgery: A Systematic Review

Abstract

Objective: Cardiac surgery carries a significant risk of complications and mortality. Artificial intelligence (AI), particularly machine learning (ML), is increasingly being explored to enhance perioperative risk prediction and support clinical decision-making. This systematic review evaluates the clinical applications, predictive performance, and limitations of AI models in cardiac surgery. Methods: PubMed and Embase were searched for studies published between January 2020 and July 2025. Of 939 records identified, 178 studies met the inclusion criteria following screening and full-text review. Included studies applied AI to predict clinical outcomes in patients undergoing cardiac surgery. Key outcomes assessed were model performance metrics and their clinical utility. Results: Among the 178 included studies, 114 (64%) were conducted in the United States or China. Most studies (n = 168, 94%) used retrospective designs and focused on adult populations. Random forest (n = 82, 46%), logistic regression (n = 82, 46%), and eXtreme Gradient Boosting (n = 70, 39%) were the most frequently used algorithms. AI applications primarily targeted the prediction of postoperative complications (n = 102, 57%) and mortality (n = 70, 39%), with common outcomes including acute kidney injury and stroke. ML models consistently outperformed traditional clinical risk scores (n = 39). SHapley Additive exPlanations was the most common interpretability method (n = 66, 37%). Only 26% of studies included external validation, and just 19% adhered to TRIPOD guidelines. Conclusions: AI models demonstrate superior predictive performance in cardiac surgery compared with traditional risk scores, but concerns regarding validation, transparency, and generalizability must be addressed to enable implementation. Graphical Abstract This is a visual representation of the abstract.