{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T13:23:50Z","timestamp":1790083430546,"version":"4.0.1"},"reference-count":71,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Objectives: The aim of this study was to synthesize current evidence on artificial intelligence (AI) adoption in cardiovascular imaging across low- and middle-income countries (LMICs), highlighting diagnostic performance, implementation barriers, and potential solutions. Methods: We conducted a systematic review of PubMed, Embase, Cochrane Library, Web of Science, and Scopus for studies evaluating AI-based echocardiography, cardiac CT, or cardiac MRI in LMICs. Articles were screened according to PRISMA guidelines, and data on diagnostic outcomes, challenges, and enabling factors were extracted and narratively synthesized. Results: Twelve studies met the inclusion criteria. AI-driven methods frequently surpassed 90% accuracy in detecting coronary artery disease, rheumatic heart disease, and left ventricular hypertrophy, often enabling task shifting to non-expert operators. Challenges included limited dataset diversity, operator dependence, infrastructure constraints, and ethical considerations. Insights from high-income countries, such as automated segmentation and accelerated imaging, suggest potential for broader AI integration in cardiac MRI and CT. Conclusions: AI holds promise for enhancing cardiovascular care in LMICs by improving diagnostic accuracy and workforce efficiency. However, multi-center data sharing, targeted training, reliable infrastructure, and robust governance are essential for sustainable adoption. This review underscores AI\u2019s capacity to bridge resource gaps in LMICs, offering practical pathways for future research, clinical practice, and policy development in global cardiovascular imaging.<\/jats:p>","DOI":"10.3390\/bioengineering12101038","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:00:32Z","timestamp":1759132832000},"page":"1038","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["From Echocardiography to CT\/MRI: Lessons for AI Implementation in Cardiovascular Imaging in LMICs\u2014A Systematic Review and Narrative Synthesis"],"prefix":"10.3390","volume":"12","author":[{"given":"Ahmed","family":"Marey","sequence":"first","affiliation":[{"name":"Department of Radiology, Sheikh Khalifa Medical City, Abu Dhabi 51900, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saba","family":"Mehrtabar","sequence":"additional","affiliation":[{"name":"Department of Radiology, Tabriz University of Medical Sciences, Tabriz 5178654714, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Afify","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Benha University, Benha 13518, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Basudha","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arcadia","family":"Trvalik","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Washington Hospital Center, MedStar Health, Washington, DC 20010, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4388-8754","authenticated-orcid":false,"given":"Sola","family":"Adeleke","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, King\u2019s College London, London SE1 7EH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6113-8335","authenticated-orcid":false,"given":"Muhammad","family":"Umair","sequence":"additional","affiliation":[{"name":"Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins Hospital, Baltimore, MD 21287, USA"},{"name":"Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,27]]},"reference":[{"key":"ref_1","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\u20132019: Update from the GBD 2019 Study","volume":"76","author":"Roth","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_2","unstructured":"WHO (2025, January 02). Cardiovascular Diseases (CVDs). 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