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.

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.

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.

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.