Artificial Intelligence in Acute Coronary Syndromes: From Early Diagnosis to Culprit Lesion Characterization, Coronary Physiology, and Risk Stratification

Artificial intelligence (AI) is being investigated across the acute coronary syndrome (ACS) care pathway, including early diagnosis, coronary lesion characterization, procedural guidance, and risk prediction. This comprehensive review examines the current evidence across the entire ACS trajectory, with particular emphasis on clinically actionable and interventional applications. AI-enabled electrocardiography (ECG) may improve detection of myocardial infarction, including occlusion myocardial infarction (OMI) that does not meet conventional STEMI criteria. Models combining clinical data and high-sensitivity troponin may improve early rule-out and rule-in. In coronary imaging, AI can quantify plaque characteristics and assist with the interpretation of OCT and IVUS, while image-derived physiology may help assess non-culprit lesions. However, better diagnostic accuracy does not necessarily translate into better revascularization decisions. AI-based models can also estimate post-ACS ischemic, bleeding, and mortality risk, although evidence that their use improves treatment or outcomes remains limited. The next priority is prospective evaluation of whether these tools improve decisions, workflow, and patient outcomes across diverse clinical settings.