Abstract
Colorectal cancer (CRC) is a prevalent malignancy, with surgery playing a key role in its treatment. However, perioperative complications, such as anastomotic leaks, infections, and mortality, can significantly affect surgical outcomes, extend hospital stays, and increase healthcare costs. Traditional risk prediction models often lack precision, leading to increased interest in artificial intelligence (AI) for improving risk stratification. This review examines the application of AI, particularly machine learning and deep learning, in predicting perioperative complications in CRC surgery. AI models have been employed to predict a variety of postoperative complications, including readmissions, surgical-site infections, anastomotic leakage, and mortality, by analyzing diverse data sources such as electronic health records, medical imaging, and preoperative markers. Despite the promising results, several challenges remain, including data quality, model generalizability, the complexity of clinical data, and ethical and regulatory concerns. The review emphasizes the need for multicenter, diverse datasets and the integration of AI into clinical workflows to improve model performance and adoption. Future efforts should focus on enhancing the transparency and interpretability of AI models to ensure their successful implementation in clinical practice, ultimately improving patient outcomes and surgical decision-making in CRC surgery.