{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T20:20:24Z","timestamp":1791490824731,"version":"4.3.4"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:00:00Z","timestamp":1787961600000},"content-version":"vor","delay-in-days":31,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Early identification of acute myocardial infarction (AMI) remains challenging, particularly in non-ST-segment elevation presentations and occluded myocardial infarction, where conventional electrocardiogram (ECG) interpretation has limited sensitivity. Artificial intelligence\u2013enabled ECG (AI-ECG) has emerged as a promising strategy to enhance early triage and diagnostic accuracy. To systematically evaluate the diagnostic performance of AI-enabled ECG algorithms for the detection of AMI, including ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI), across diverse clinical settings. This diagnostic systematic review and meta-analysis was conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420261292271). PubMed, Embase, and Cochrane CENTRAL were searched through January 2026. Studies evaluating AI-based ECG models for AMI detection and reporting sufficient data to reconstruct 2 \u00d7 2 contingency tables were included. Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated using random-effects models (restricted maximum likelihood). Summary receiver operating characteristic (SROC) curves and area under the curve (AUC) were generated. Pre-specified subgroup analyses were performed for STEMI and NSTEMI Ten observational studies comprising 94 510 participants were included. For overall AMI detection,. AI-ECG demonstrated a pooled sensitivity of 89.4% (95% CI, 79.7\u201394.8) and specificity of 96% (95% CI, 91.2\u201398.2). The pooled NPV was 98.7% (95% CI, 94.1\u201399.7), and the pooled PPV was 73.3% (95% CI, 50.2\u201388.2). The SROC AUC was 0.97 (95% CI, 0.92\u20130.98). In STEMI, pooled sensitivity and specificity were 94.4% and 97.5%, respectively (AUC 0.98). In NSTEMI, pooled sensitivity was lower at 65.0%, with specificity of 87.5% and an AUC of 0.71. Heterogeneity was substantial, particularly among NSTEMI cohorts. AI-enabled ECG demonstrates high sensitivity and consistently excellent negative predictive value for AMI detection, supporting its role as a scalable, non-invasive triage adjunct at first medical contact. These findings highlight the potential of AI-ECG to facilitate early rule-out strategies and improve prioritization of patients requiring urgent ischaemic evaluation. Beyond diagnostic accuracy, AI-ECG may support probabilistic risk stratification and integration into early clinical decision-making pathways.<\/jats:p>","DOI":"10.1093\/ehjdh\/ztag075","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T11:44:27Z","timestamp":1782215067000},"source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence\u2013based ECG as a triage tool for acute myocardial infarction: a diagnostic systematic review and meta-analysis"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3261-863X","authenticated-orcid":false,"given":"Giulia Caldeira","family":"Gaelzer","sequence":"first","affiliation":[{"name":"Pontifical Catholic University of Paran\u00e1 , Curitiba, Paran\u00e1 ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1461-7185","authenticated-orcid":false,"given":"Pedro Gomes","family":"Batista","sequence":"additional","affiliation":[{"name":"Federal University of Para\u00edba , Jo\u00e3o Pessoa, Para\u00edba ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8939-5026","authenticated-orcid":false,"given":"Marcela Vasconcelos","family":"Montenegro","sequence":"additional","affiliation":[{"name":"University of Pernambuco, Recife, Pernambuco ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6403-2218","authenticated-orcid":false,"given":"Railla Raquel Albino","family":"dos Santos Silva","sequence":"additional","affiliation":[{"name":"Federal University of Cear\u00e1 , Fortaleza, Cear\u00e1 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