Artificial Intelligence in Radiotherapy-Associated Cardiovascular Toxicity: A Systematic Review of Predictive and Imaging Applications

Background Cardiovascular toxicity (CVT) is a major effect of radiation therapy (RT) and a contributor to morbidity and mortality among cancer survivors. Artificial intelligence (AI) may improve early detection, risk stratification, and RT planning to mitigate cardiac exposure, but the current evidence has not been comprehensively synthesized. The main objective of this study is to analyze and assess the quality of literature applied AI assessments of CVT in populations receiving RT. Methods PRISMA-guided systematic review of PubMed, Ovid EMBASE, Cochrane Library, and Web of Science was conducted through October 1, 2025. Eligible studies were original human research in English applying AI to cardiovascular outcomes or imaging in cancer populations receiving RT. Predictive-model studies were assessed using TRIPOD + AI for quality and PROBAST for risk-of-bias. Imaging-AI studies were assessed using CLAIM and QUADAS-2 for quality and risk-of-bias respectively. Results Sixty-five studies were included and clustered into two groups: (1) AI prediction of RT-associated CVT (n = 31, 48%) and (2) AI-based cardiovascular imaging (n = 34, 52%). Deep learning was the most frequent approach (45/65, 69%) especially in imaging and showed highest performance (median AUC = 0.82 & sensitivity = 0.83) in prediction. Predictive models lacked calibration assessment (3/31, 10%), and external validation (6/31, 19%). TRIPOD + AI adherence averaged 79% (SD 22.68%), while PROBAST rated 97% at high overall risk-of-bias. Imaging models demonstrated strong performance (overall median DSC = 0.85, range: 0.76–0.94) particularly for larger cardiac structures, whereas coronary artery segmentation remained challenging. CLAIM adherence averaged 71%, and QUADAS-2 judged 82% at high risk-of-bias. Conclusion AI approaches in radiation-associated cardio-oncology are promising but not yet implementation-ready. Future work should prioritize standardized endpoints, robust external validation, calibration and clinical utility evaluation, shared high-quality imaging annotations, and prospective integration into clinical trials.