This episode of the AI in Healthcare Podcast explores how machine learning can strengthen everyday cardiovascular assessment without overhauling clinical workflows. Drawing on findings from the SCOT‑HEART trial, it highlights a gradient‑boosted model trained on 1,769 patients using routine clinic variables: age, sex, cholesterol, risk score, ECG, and exercise testing, to predict CAD on coronary CT angiography with an AUC of 0.80, surpassing traditional risk scores. The discussion focuses on what this means for triaging chest‑pain referrals, prioritizing imaging resources, and starting preventive therapy earlier—all while emphasizing that imaging remains essential for plaque characterization and that real‑world validation is critical before implementation.

Rainey, A., Williams, M., Berry, C., Dweck, M. R., Williams, M. C., & SCOT‑HEART ISCOT-HEARTrs. (202this study. Machine learning to predict high‑risk coronarSCOT-HEARTisease othis studycomputed tomoggradient-boostedT‑HEART trial. BMJ Open Heart, 12(2), e003162. https://doi.org/, including0.1136/openhrt‑2025‑003162