{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:52:40Z","timestamp":1792173160336,"version":"4.4.0"},"reference-count":26,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Cardiovascular disease (CVD) remains the leading cause of death worldwide. While clinical scores provide modest predictive accuracy, the electrocardiogram (ECG) is rarely leveraged for prognosis. Artificial intelligence (AI) and deep learning now enable extraction of high-dimensional features from ECG waveforms for predicting cardiovascular outcomes. Our objective was to systematically evaluate the prognostic performance of AI-enabled ECG models for predicting all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events (MACE). We searched PubMed, Cochrane CENTRAL, IEEE Xplore, and Google Scholar through November 2025 for studies using AI\/ML models with 12-lead or single-lead ECGs to predict all-cause mortality, cardiovascular mortality, or MACE in adults (\u226518 years old). Eligible studies included adults (\u226518 years) using ECG as primary input. Two reviewers independently screened and extracted data. Narrative synthesis was conducted due to heterogeneity. Thirteen studies were included (~5.9 million patients). Eleven reported all-cause mortality (8 short-term: 30-day to 1-year), four reported cardiovascular mortality, and eight assessed MACE (4 composite MACE, 4 individual components). Models predominantly used convolutional neural networks [CNNs] on raw ECG waveforms. C-statistics ranged 0.70\u20130.89 for mortality prediction. Definitions and follow-up durations varied, limiting direct comparison. AI-ECG models demonstrate strong potential for predicting mortality and cardiovascular events with consistent performance and scalability. However, prospective validation and standardization are essential before clinical implementation.<\/jats:p>","DOI":"10.1371\/journal.pone.0360756","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:26:42Z","timestamp":1792171602000},"page":"e0360756","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence-enhanced electrocardiography for predicting mortality and major adverse cardiovascular events: A systematic review and narrative synthesis"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8319-8536","authenticated-orcid":true,"given":"Elhaitham Yasir Awad","family":"Musa","sequence":"first","affiliation":[{"name":"National Ribat University","place":["Khartoum, Sudan"],"department":["Faculty of Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amar Yasir Awad","family":"Musa","sequence":"additional","affiliation":[{"name":"National Ribat University","place":["Khartoum, Sudan"],"department":["Faculty of Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"key":"pone.0360756.ref001","volume-title":"Cardiovascular diseases (CVDs)","author":"World Health Organization","year":"2025"},{"issue":"18","key":"pone.0360756.ref002","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1161\/01.CIR.97.18.1837","article-title":"Prediction of coronary heart disease using risk factor categories","volume":"97","author":"PW Wilson","year":"1998","journal-title":"Circulation"},{"key":"pone.0360756.ref003","article-title":"2013 ACC\/AHA guideline on the assessment of cardiovascular risk: A report of the American College of Cardiology\/American Heart Association Task Force on Practice Guidelines","volume":"129","author":"DC 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Raghunath","year":"2020","journal-title":"Nat Med"},{"issue":"1","key":"pone.0360756.ref010","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1038\/s41746-023-00765-3","article-title":"Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms","volume":"6","author":"W Sun","year":"2023","journal-title":"NPJ Digit Med"},{"key":"pone.0360756.ref011","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1038\/s41746-023-00916-6","article-title":"A deep learning-based ECG risk score for long-term cardiovascular death and disease","volume":"6","author":"JW Hughes","year":"2023","journal-title":"NPJ Digit Med"},{"issue":"1","key":"pone.0360756.ref012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-024-01410-3","article-title":"A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction","volume":"8","author":"C-H 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Kanaji","year":"2024","journal-title":"J Am Heart Assoc"},{"issue":"7","key":"pone.0360756.ref026","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1038\/s41569-020-00503-2","article-title":"Artificial intelligence-enhanced electrocardiography in cardiovascular disease management","volume":"18","author":"KC Siontis","year":"2021","journal-title":"Nat Rev Cardiol"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0360756","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:26:49Z","timestamp":1792171609000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0360756"}},"subtitle":[],"editor":[{"given":"Chiara","family":"Lazzeri","sequence":"first","affiliation":[{"name":"Azienda Ospedaliero Universitaria 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