{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:09:54Z","timestamp":1790633394178,"version":"4.1.0"},"reference-count":48,"publisher":"BMJ","issue":"2","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"unspecified","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Open Heart"],"accepted":{"date-parts":[[2026,6,29]]},"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:p>Artificial intelligence applied to the ECG is expanding the clinical role of this widely available diagnostic tool beyond conventional waveform interpretation by enabling identification of electrophysiological patterns associated with cardiovascular structure, function and risk. Within this evolving framework, the artificial intelligence enhanced ECG is emerging as a scalable digital biomarker platform supporting screening, diagnosis and prognostic stratification across the cardiovascular disease continuum.<\/jats:p>\n                  <jats:p>This narrative review synthesises current evidence on the clinical applications of artificial intelligence enhanced ECG, including detection of cardiac rhythm disorders, early identification of structural heart disease, decision support in acute coronary syndromes, prediction of clinical outcomes and population level cardiovascular screening using wearable technologies. Deep learning models applied to standard and simplified electrocardiographic recordings demonstrate strong diagnostic performance in identifying atrial fibrillation, left ventricular dysfunction, hypertrophic cardiomyopathy, cardiac amyloidosis and heart failure with preserved ejection fraction. These approaches also enable estimation of long-term cardiovascular risk from apparently normal tracings and support identification of disease patterns extending beyond overt cardiac conditions.<\/jats:p>\n                  <jats:p>Despite these advances, clinical implementation remains limited by the need for prospective multicentre validation, improved model interpretability, standardised evaluation strategies and integration into routine clinical workflows. This review highlights current validation gaps and outlines key opportunities for translation of artificial intelligence-enhanced ECG into digital cardiology practice.<\/jats:p>","DOI":"10.1136\/openhrt-2026-004211","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T11:05:46Z","timestamp":1783595146000},"page":"e004211","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":4,"title":["AI-enhanced electrocardiography as a digital biomarker platform in cardiovascular medicine: clinical applications, validation gaps and future implementation pathways"],"prefix":"10.1136","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0874-9735","authenticated-orcid":false,"given":"Damiano","family":"Venturiello","sequence":"first","affiliation":[{"name":"Department of Cardiology, Celio Military Hospital, Rome, Lazio, 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