The Digital Revolution in Cardiac Ischemia: Artificial Intelligence (AI)-Enhanced Detection, Diagnosis, and Risk Stratification

The digital revolution in cardiac ischemia has changed with ongoing applications of artificial intelligence (AI) to overcome the limitations of traditional diagnostic tools and risk scores. Recent advances in machine learning and deep learning (DL) have enabled convolutional neural networks to detect myocardial infarction with high accuracy, including subtle non-ST-elevation occlusion events that often elude human readers. AI-driven analyses of coronary CT angiography now automate plaque quantification and stenosis assessment, while DL-based fractional flow reserve computation reduces evaluation time without compromising diagnostic performance. In cardiac MRI and perfusion imaging, AI algorithms perform real-time myocardium segmentation and ischemia detection at expert levels. Wearable device integration offers continuous out-of-hospital monitoring for the early detection of ischemic events. Despite challenges related to algorithmic bias, clinical workflow integration, and validation across diverse populations, current evidence demonstrates that AI-enhanced tools not only match but often surpass traditional methods and expert interpretation. As multimodal AI integration, personalized risk prediction models, and advanced wearable technologies continue to evolve, AI promises to transform cardiac ischemia management by enabling earlier detection, more accurate diagnosis, and refined risk stratification, ultimately improving patient outcomes.