{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:06:38Z","timestamp":1780081598025,"version":"3.54.0"},"reference-count":80,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Conventional echocardiography traditionally relies on population-derived reference values and dichotomous classification schemes that do not fully account for individual patient characteristics or disease complexity. Artificial intelligence (AI) is driving a paradigm shift toward precision echocardiography by enabling patient-specific cardiovascular assessment through the integration of phenotypic, clinical, and biological data. This review examines how AI is transforming echocardiography from a population-based test into a patient-specific assessment tool that supports precision cardiovascular care. It highlights three clinical applications with potential clinical impact: Heart Failure with Preserved Ejection Fraction phenogrouping for targeted therapy selection, cardio-oncology surveillance with individualized cardiotoxicity risk prediction, and cardiomyopathy risk stratification for personalized sudden cardiac death prevention. For each application, it describes the clinical challenge, the AI-enabled precision solution, and its potential clinical impact. It also outlines a practical roadmap for clinical adoption. Precision echocardiography, powered by AI, holds promise for transforming cardiovascular imaging and diagnostics by enabling more patient-specific assessment, earlier disease detection, and personalized therapeutic strategies.<\/jats:p>","DOI":"10.3390\/diagnostics16050694","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:57:13Z","timestamp":1772125033000},"page":"694","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-Enabled Precision Echocardiography: Toward Personalized Cardiovascular Care"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9951-8832","authenticated-orcid":false,"given":"Lamia","family":"Al Saikhan","sequence":"first","affiliation":[{"name":"Department of Cardiac Technology, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1093\/ehjci\/jev014","article-title":"Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging","volume":"16","author":"Lang","year":"2015","journal-title":"Eur. 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