{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T19:12:32Z","timestamp":1785179552552,"version":"3.55.0"},"reference-count":157,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Cardiovascular imaging is essential in the diagnosis, phenotyping and prognostic assessment of cardiovascular disease. However, longstanding limitations constrain the accuracy, throughput, and scalability of cardiovascular imaging techniques. Artificial intelligence (AI) has demonstrated a diverse range of potential benefits across modalities, including echocardiography, computerised tomography, nuclear imaging, and magnetic resonance imaging. These benefits include automated quantification of key heart parameters, ability to improve traditional disease detection and phenotyping, and image reconstruction. While the use of AI in clinical workflows is still largely emerging, its significance is becoming established through numerous promising studies. The evidence reviewed indicates that AI can meaningfully enhance disease management, clinical operations and patient experience when used alongside physician expertise. However, several challenges restrict the widespread clinical implementation of AI, including a lack of robust prospective evidence, regulatory hurdles, bias in training datasets, and ethical drawbacks such as data privacy and accountability. Future developments should prioritise large-scale prospective and multicentre validation and address practical and ethical barriers to ensure AI can be utilised safely and effectively in clinical settings. This narrative review comprehensively analyses advances in AI-driven cardiovascular imaging with a focus on clinical implementation.<\/jats:p>","DOI":"10.3390\/life16030507","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T11:50:36Z","timestamp":1773921036000},"page":"507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?"],"prefix":"10.3390","volume":"16","author":[{"given":"Archit","family":"Singhal","sequence":"first","affiliation":[{"name":"Oxford Heart Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK"},{"name":"Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiffany","family":"Bowyer-Howell","sequence":"additional","affiliation":[{"name":"University of Oxford Medical School, John Radcliffe Hospital, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1989-947X","authenticated-orcid":false,"given":"Nikant","family":"Sabharwal","sequence":"additional","affiliation":[{"name":"Oxford Heart Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Lewis","sequence":"additional","affiliation":[{"name":"Oxford Heart Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK"},{"name":"Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5386-9645","authenticated-orcid":false,"given":"Andrew","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Jersey General Hospital, Gloucester Street, St. Helier JE1 3QS, Jersey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Rider","sequence":"additional","affiliation":[{"name":"Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7650-0963","authenticated-orcid":false,"given":"John","family":"Henry","sequence":"additional","affiliation":[{"name":"Oxford Heart Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK"},{"name":"Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK"},{"name":"Department of Cardiology, Jersey General Hospital, Gloucester Street, St. Helier JE1 3QS, Jersey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"qyae136","DOI":"10.1093\/ehjimp\/qyae136","article-title":"Advancements and applications of artificial intelligence in cardiovascular imaging: A comprehensive review","volume":"2","author":"Fortuni","year":"2024","journal-title":"Eur. 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