One in four heart attack patients is currently being missed at emergency triage — not because of physician error, but because the dominant diagnostic framework itself has a structural blind spot. That gap may be narrowing, with compelling evidence emerging that artificial intelligence applied to standard electrocardiography can substantially close the detection deficit for occlusive myocardial infarction.
This state-of-the-art review, drawing on multicenter registry and prospective cohort data from the Queen of Hearts registry, ROMIAE, and DIFOCCULT-3 studies, examines AI-ECG performance across two distinct clinical tasks that the review argues must not be conflated. For detecting occlusion myocardial infarction (OMI) requiring emergent catheterization lab activation, AI-ECG achieved a sensitivity of 92% against 71% for standard STEMI/NSTEMI classification — a 21 percentage-point gain. Simultaneously, false-positive cath lab activations among biomarker-negative patients fell by up to 91%, addressing a major operational and patient-safety burden. For the separate task of ruling out acute MI entirely, AI-ECG combined with high-sensitivity troponin and clinical risk scoring reached a negative predictive value near 99%, suggesting meaningful triage acceleration when integrated rather than used standalone.
The distinction the review draws between OMI detection and MI rule-out is clinically important and often underappreciated. ECG-based AI has historically been validated on retrospective datasets with known outcomes, creating optimistic performance estimates that don't survive real-world deployment. The prospective and multicenter data cited here represent a meaningful maturation of the evidence base, though widespread clinical adoption still hinges on unresolved questions around liability frameworks, alert fatigue, and algorithm generalizability across ECG hardware vendors. The proposed 'Second Opinion' model — AI as augmentation rather than replacement of physician judgment — is a prudent framing that aligns with emerging regulatory guidance on clinical decision support tools. For cardiovascular risk reduction at the population level, earlier and more accurate OMI identification has direct implications for myocardial salvage and long-term cardiac function. This review is best characterized as confirmatory and synthesizing rather than paradigm-shifting, but the aggregated effect sizes are large enough to support accelerated implementation trials.