This study evaluates the real-world performance of an AI-based ECG interpretation tool for detecting STEMI in patients undergoing emergent catheterization laboratory activation, using angiographic findings as the reference standard. Compared with standard clinical ECG interpretation, the AI model achieved higher sensitivity and specificity, significantly reducing false-positive activations while maintaining strong performance even in challenging cases such as arrhythmias or conduction abnormalities. These results suggest that AI-assisted ECG analysis can improve diagnostic accuracy, optimize triage, and support faster, more appropriate decision-making in acute coronary syndrome management.
Published in: JACC Advances
Published on: 23 March 2026
Background
Artificial intelligence (AI)–based electrocardiogram (ECG) analysis has emerged as a promising adjunct to human ECG interpretation in suspected ST-segment elevation myocardial infarction (STEMI).
Methods
Consecutive patients were gathered from a multicenter U.S. STEMI registry (2018-2022) and categorized into 3 clinical cohorts based on the presence or absence of angiographic culprit and troponin elevation: acute myocardial infarction (AMI) with culprit, AMI without culprit, and no-AMI. Cardiac catheterization laboratory-activating ECGs were analyzed using an AI-ECG model trained to identify acute coronary occlusion and classified as occlusion myocardial infarction, OMI(+) or not, OMI(−).
Results
The study included 2,523 patients, 68.3% male, with a median age of 63 years. AMI with culprit was present in 2076 (82.3%), AMI without culprit in 314 (12.4%), and no-AMI in 133 (5.3%). Among AMI with culprit patients, the model correctly identified 93.8% as OMI(+). Sensitivity for TIMI flow 0/1, 2, and 3 was 96.3%, 93.1%, and 86.9%, respectively; P < 0.001. The model correctly identified 79.7% of no-AMI patients as OMI(−). The AUCROC was 0.952 (95% CI: 0.924-0.966). The AMI without culprit cohort included takotsubo syndrome OMI(+) = 78%, MI with nonobstructive coronary arteries OMI(+) = 61%, and myopericarditis OMI(+) = 67%.
Conclusion
In suspected STEMI, this AI-ECG model correctly identified nearly all patients with acute coronary obstruction and most of those without AMI. If prospectively validated, this approach could improve the management of patients with suspected AMI.
