{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T05:28:45Z","timestamp":1788931725250,"version":"build-2803163510"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007184","name":"Yale School of Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007184","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HL167858"],"award-info":[{"award-number":["R01HL167858"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG089981"],"award-info":[{"award-number":["R01AG089981"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K23HL153775"],"award-info":[{"award-number":["K23HL153775"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1F32HL170592"],"award-info":[{"award-number":["1F32HL170592"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000862","name":"Doris Duke Charitable Foundation","doi-asserted-by":"publisher","award":["2022060"],"award-info":[{"award-number":["2022060"]}],"id":[{"id":"10.13039\/100000862","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Timely, accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and managing patients. However, this often relies on expert interpretation, a major bottleneck in low-resource settings. We developed and validated ECG-GPT, a format-independent vision encoder\u2013decoder model that generates expert-level interpretations from 12-lead ECG images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>We developed ECG-GPT using 12-lead ECGs and their corresponding diagnosis statements performed at a large US health system between 2000 and 2022. Using structured clinical assessment, semantic similarity, and conventional metrics, we validated ECG-GPT across seven distinct health settings, including three large and diverse US health systems, ECGs from Minas Gerais, Brazil, the UK Biobank, the Germany-based PTB-XL dataset, and a community hospital in Missouri. In total, 2.9 million ECGs were used for model development, and 4.1 million ECGs for validation. The model performed well in clinical assessment across 26 extracted labels, with diagnostic accuracy ranging from 0.93 to 0.99. For rhythm abnormalities, including atrial fibrillation, sinus tachycardia, sinus bradycardia, premature atrial contractions, and premature ventricular contractions, AUROCs ranged from 0.80 to 0.95. For conduction abnormalities, including left bundle branch block, right bundle branch block, first degree atrioventricular block, left anterior fascicular block, and left posterior fascicular block, AUROCs ranged from 0.88 to 0.96. ECG-GPT identified the full context of diagnosis statements with allied conditions with a median pairwise similarity of 0.90, significantly greater than baseline (P &amp;lt; 0.001). Results were comparable across external validation sites.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed and validated a vision encoder-decoder model that generates expert-level interpretations from ECG images, a scalable strategy for accessible automated ECG analysis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztag031","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T12:45:10Z","timestamp":1771418710000},"source":"Crossref","is-referenced-by-count":5,"title":["Artificial intelligence-based automated interpretation of images of electrocardiograms: development and multinational validation of ECG-GPT"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3812-3260","authenticated-orcid":false,"given":"Akshay","family":"Khunte","sequence":"first","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine , 195 Church St, 6th Floor, New Haven, CT 06510 ,","place":["USA"]},{"name":"NYU Grossman School of Medicine , New York, NY ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-1203","authenticated-orcid":false,"given":"Veer","family":"Sangha","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine , 195 Church St, 6th Floor, New Haven, CT 06510 ,","place":["USA"]},{"name":"Department of Engineering Science, Oxford University , Oxford ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4362-0720","authenticated-orcid":false,"given":"Evangelos K","family":"Oikonomou","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine , 195 Church St, 6th Floor, New Haven, CT 06510 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5664-4126","authenticated-orcid":false,"given":"Lovedeep S","family":"Dhingra","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine , 195 Church St, 6th Floor, New Haven, CT 06510 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Outcomes Research and Evaluation, Yale-New Haven Hospital , New Haven, CT ,","place":["USA"]},{"name":"Department of Computer Science & Engineering, Texas A&M University , College Station, TX ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1278-6245","authenticated-orcid":false,"given":"Deepak L","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Mount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai , New York, NY ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2046-127X","authenticated-orcid":false,"given":"Harlan M","family":"Krumholz","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine , 195 Church St, 6th Floor, New Haven, CT 06510 ,","place":["USA"]},{"name":"Center for Outcomes Research and Evaluation, Yale-New Haven Hospital , New Haven, CT ,","place":["USA"]},{"name":"Department of Health Policy and Management, Yale School of Public Health , New Haven, CT ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1122-7695","authenticated-orcid":false,"given":"Sadeer","family":"Al-Kindi","sequence":"additional","affiliation":[{"name":"Center for Cardiovascular Computational & Precision Health, Houston Methodist DeBakey Heart & Vascular Center , Houston, TX ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6319-4314","authenticated-orcid":false,"given":"Girish N","family":"Nadkarni","sequence":"additional","affiliation":[{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai , New York, NY 10029 ,","place":["USA"]},{"name":"The Hasso Plattner Institute for Digital Health at Mount Sinai , New York, NY 10029 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