Artificial Intelligence for Coronary Artery Disease Prediction Using ECG and CCTA: A Systematic Review

Abstract

Coronary artery disease (CAD) is the leading cause of death worldwide, highlighting the need for more reliable and efficient diagnostic tools beyond conventional methods. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown strong potential for detecting obstructive CAD by learning complex patterns from electrocardiogram (ECG) and coronary computed tomography angiography (CCTA) data. This rapid systematic review assesses and compares the diagnostic performance and methodological quality of AI models built for CAD prediction using ECG and CCTA data. A systematic search following PRISMA-ScR guidelines was conducted for primary studies published between 2021 and 2025. Eleven studies were included, six using ECG data and five using CCTA data. Methodological quality was evaluated using the PROBAST+AI tool. ECG-based models achieved AUCs of 0.72–0.961 and CCTA-based models showed slightly stronger top-end performance, with AUCs of 0.77–0.97. External validation was uncommon in both groups, applied in only 40% of CCTA studies and 33% of ECG studies, so neither modality demonstrated clearly greater validation maturity. Despite these strong results, PROBAST+AI assessment revealed a high risk of bias in 90.9% of the included studies, largely due to weaknesses in the analysis domain, including poor handling of missing data and the absence of model calibration reporting. AI models show strong diagnostic accuracy for CAD across both modalities, although external validation was limited and applied in a minority of studies. However, the widespread methodological bias means these tools should currently support clinical decision-making rather than replace standard diagnostic methods. Future studies should focus on prospective multicentre validation and the use of multimodal data.