{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T06:14:07Z","timestamp":1788502447811,"version":"build-2803163510"},"reference-count":48,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, evolving from theoretical promise to practical tools that improve diagnostic accuracy, streamline workflow, and enhance patient outcomes. Machine learning (ML) and deep learning systems now support automated image interpretation, disease detection, physiologic signal analysis, and individualized risk prediction. Large language models further reduce documentation burden and enable more efficient clinical communication. This review summarizes current applications of AI across cardiovascular care, with an example on the role of AI in predictive analytics in liver transplant candidates, an area where early risk identification may significantly alter outcomes. The article also explores emerging innovations, including the potential role of quantum computing, that may define the next era of cardiovascular medicine.<\/jats:p>","DOI":"10.4103\/heartviews.heartviews_41_26","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T06:00:06Z","timestamp":1788501606000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["The Emerging Role of Artificial Intelligence in Cardiovascular Care: From Imaging to Predictive Analytics"],"prefix":"10.4103","volume":"27","author":[{"given":"Taha","family":"Hatab","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maan","family":"Fares","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,1]]},"reference":[{"key":"R1-20260904","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.techfore.2016.08.019","article-title":"The future of employment: How susceptible are jobs to computerisation?","volume":"114","author":"Frey","year":"2017","journal-title":"Technol Forecast Soc Change"},{"key":"R4-20260904","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat Med"},{"key":"R5-20260904","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N Engl J Med"},{"key":"R6-20260904","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat Med"},{"key":"R7-20260904","doi-asserted-by":"crossref","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","article-title":"The potential for artificial intelligence in healthcare","volume":"6","author":"Davenport","year":"2019","journal-title":"Future Healthc J"},{"key":"R8-20260904","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990-2019: Update from the GBD 2019 study","volume":"76","author":"Roth","year":"2020","journal-title":"J Am Coll Cardiol"},{"key":"R9-20260904","doi-asserted-by":"crossref","first-page":"e93","DOI":"10.1161\/CIR.0000000000001123","article-title":"Heart disease and stroke statistics-2023 update: A report from the American Heart Association","volume":"147","author":"Tsao","year":"2023","journal-title":"Circulation"},{"key":"R10-20260904","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-based AI for beat-to-beat assessment of cardiac function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"R11-20260904","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","article-title":"An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction","volume":"394","author":"Attia","year":"2019","journal-title":"Lancet"},{"key":"R12-20260904","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1001\/jamacardio.2021.0122","article-title":"Use of machine learning models to predict death after acute myocardial infarction","volume":"6","author":"Khera","year":"2021","journal-title":"JAMA Cardiol"},{"key":"R13-20260904","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","article-title":"Fully automated echocardiogram interpretation in clinical practice","volume":"138","author":"Zhang","year":"2018","journal-title":"Circulation"},{"key":"R14-20260904","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning","volume":"2","author":"Poplin","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"R15-20260904","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.1016\/j.jacc.2018.03.521","article-title":"Artificial intelligence in cardiology","volume":"71","author":"Johnson","year":"2018","journal-title":"J Am Coll Cardiol"},{"key":"R16-20260904","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"R18-20260904","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/mind\/LIX.236.433","article-title":"Computing machinery and intelligence","volume":"59","author":"Turing","year":"1950","journal-title":"Mind"},{"key":"R19-20260904","first-page":"12","article-title":"A Proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955","volume":"27","author":"McCarthy","year":"2006","journal-title":"AI Mag"},{"key":"R20-20260904","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"R28-20260904","doi-asserted-by":"crossref","first-page":"753","DOI":"10.7326\/M16-0961","article-title":"Allocation of physician time in ambulatory practice: A time and motion study in 4 specialties","volume":"165","author":"Sinsky","year":"2016","journal-title":"Ann Intern Med"},{"key":"R29-20260904","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1016\/j.mayocp.2016.05.007","article-title":"Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction","volume":"91","author":"Shanafelt","year":"2016","journal-title":"Mayo Clin Proc"},{"key":"R30-20260904","first-page":"e73994","article-title":"Evaluating the impact of artificial intelligence (AI) on clinical documentation efficiency and accuracy across clinical settings: A scoping review","volume":"16","author":"Lee","year":"2024","journal-title":"Cureus"},{"key":"R31-20260904","doi-asserted-by":"crossref","first-page":"e258614","DOI":"10.1001\/jamanetworkopen.2025.8614","article-title":"Evaluation of an ambient artificial intelligence documentation platform for clinicians","volume":"8","author":"Stults","year":"2025","journal-title":"JAMA Netw Open"},{"key":"R32-20260904","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1001\/jama.2024.21772","article-title":"Translating AI for the clinician","volume":"332","author":"Patel","year":"2024","journal-title":"JAMA"},{"key":"R33-20260904","doi-asserted-by":"crossref","first-page":"3270","DOI":"10.1038\/s41591-025-03983-2","article-title":"Generative artificial intelligence in medicine","volume":"31","author":"Teo","year":"2025","journal-title":"Nat Med"},{"key":"R34-20260904","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"R35-20260904","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1016\/j.jacc.2018.12.054","article-title":"Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review","volume":"73","author":"Dey","year":"2019","journal-title":"J Am Coll Cardiol"},{"key":"R36-20260904","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1016\/j.jacc.2015.07.052","article-title":"Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: The FAST-EFs multicenter study","volume":"66","author":"Knackstedt","year":"2015","journal-title":"J Am Coll Cardiol"},{"key":"R37-20260904","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1016\/j.jacc.2016.08.062","article-title":"Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography","volume":"68","author":"Narula","year":"2016","journal-title":"J Am Coll Cardiol"},{"key":"R38-20260904","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1109\/TMI.2019.2953054","article-title":"Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography","volume":"39","author":"Zreik","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"key":"R39-20260904","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.zemedi.2024.01.002","article-title":"Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks","volume":"35","author":"Izadi","year":"2025","journal-title":"Z Med Phys"},{"key":"R40-20260904","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3389\/fcvm.2020.00025","article-title":"Deep learning for cardiac image segmentation: A review","volume":"7","author":"Chen","year":"2020","journal-title":"Front Cardiovasc Med"},{"key":"R41-20260904","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s12968-018-0471-x","article-title":"Automated cardiovascular magnetic resonance image analysis with fully convolutional networks","volume":"20","author":"Bai","year":"2018","journal-title":"J Cardiovasc Magn Reson"},{"key":"R42-20260904","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1038\/s41591-024-02971-2","article-title":"Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging","volume":"30","author":"Wang","year":"2024","journal-title":"Nat Med"},{"key":"R43-20260904","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1016\/j.jacc.2017.03.571","article-title":"Artificial intelligence in precision cardiovascular medicine","volume":"69","author":"Krittanawong","year":"2017","journal-title":"J Am Coll Cardiol"},{"key":"R44-20260904","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1161\/CIRCULATIONAHA.110.937409","article-title":"TRUST Investigators. Efficacy and safety of automatic remote monitoring for implantable cardioverter-defibrillator follow-up: The Lumos-T safely reduces routine office device follow-up (TRUST) trial","volume":"122","author":"Varma","year":"2010","journal-title":"Circulation"},{"key":"R45-20260904","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1056\/NEJMoa1901183","article-title":"Large-scale assessment of a smartwatch to identify atrial fibrillation","volume":"381","author":"Perez","year":"2019","journal-title":"N Engl J Med"},{"key":"R46-20260904","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.amjcard.2013.04.017","article-title":"Diagnostic utility of a novel leadless arrhythmia monitoring device","volume":"112","author":"Turakhia","year":"2013","journal-title":"Am J Cardiol"},{"key":"R47-20260904","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/S0140-6736(14)61176-4","article-title":"Implant-based multiparameter telemonitoring of patients with heart failure (IN-TIME): A randomised controlled trial","volume":"384","author":"Hindricks","year":"2014","journal-title":"Lancet"},{"key":"R48-20260904","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1016\/j.jacc.2010.12.012","article-title":"The CONNECT (clinical evaluation of remote notification to reduce time to clinical decision) trial: The value of wireless remote monitoring with automatic clinician alerts","volume":"57","author":"Crossley","year":"2011","journal-title":"J Am Coll Cardiol"},{"key":"R49-20260904","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1002\/hep.31647","article-title":"Cardiac risk assessment in liver transplant candidates: Current controversies and future directions","volume":"73","author":"Barman","year":"2021","journal-title":"Hepatology"},{"key":"R50-20260904","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1161\/CIR.0000000000000105","article-title":"2014 ACC\/AHA guideline on perioperative cardiovascular evaluation and management of patients undergoing noncardiac surgery: Executive summary: A report of the American College of Cardiology\/American Heart Association task force on practice guidelines","volume":"130","author":"Fleisher","year":"2014","journal-title":"Circulation"},{"key":"R51-20260904","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1053\/j.jvca.2021.02.006","article-title":"Machine learning models to predict major adverse cardiovascular events after orthotopic liver transplantation: A cohort study","volume":"35","author":"Jain","year":"2021","journal-title":"J Cardiothorac Vasc Anesth"},{"key":"R52-20260904","doi-asserted-by":"crossref","first-page":"4556","DOI":"10.1093\/eurheartj\/ehaa159","article-title":"The \u201cdigital twin\u201d to enable the vision of precision cardiology","volume":"41","author":"Corral-Acero","year":"2020","journal-title":"Eur Heart J"},{"key":"R53-20260904","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3390\/genes17010006","article-title":"The evolving role of artificial intelligence in medical genetics: Advancing healthcare, research, and biosafety management","volume":"17","author":"Wu","year":"2025","journal-title":"Genes (Basel)"},{"key":"R54-20260904","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1038\/s41746-025-01501-9","article-title":"Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure","volume":"8","author":"Gu","year":"2025","journal-title":"NPJ Digit Med"},{"key":"R55-20260904","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1186\/s12911-024-02653-6","article-title":"Trustworthy and ethical AI-enabled cardiovascular care: A rapid review","volume":"24","author":"Mooghali","year":"2024","journal-title":"BMC Med Inform Decis Mak"},{"key":"R56-20260904","doi-asserted-by":"crossref","first-page":"100189","DOI":"10.1016\/j.mcpdig.2024.100189","article-title":"Toward safe and ethical implementation of health care artificial intelligence: Insights from an academic medical center","volume":"3","author":"Stroud","year":"2025","journal-title":"Mayo Clin Proc Digit Health"},{"key":"R57-20260904","doi-asserted-by":"crossref","first-page":"4152","DOI":"10.1097\/MS9.0000000000003349","article-title":"A narrative review on ethical considerations and challenges in AI-driven cardiology","volume":"87","author":"Patel","year":"2025","journal-title":"Ann Med Surg (Lond)"},{"key":"R58-20260904","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.mcpdig.2024.08.008","article-title":"Embedding internal accountability into health care institutions for safe, effective, and ethical implementation of artificial intelligence into medical practice: A mayo clinic case study","volume":"2","author":"Loufek","year":"2024","journal-title":"Mayo Clin Proc Digit Health"}],"container-title":["Heart Views"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/heartviews.heartviews_41_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T06:00:12Z","timestamp":1788501612000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/heartviews.heartviews_41_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.4103\/heartviews.heartviews_41_26","relation":{},"ISSN":["1995-705X","0976-5123"],"issn-type":[{"value":"1995-705X","type":"print"},{"value":"0976-5123","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2026-02-22","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-04","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}