{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:03:25Z","timestamp":1775142205328,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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>Hypertension remains a leading modifiable risk factor for cardiovascular morbidity and mortality. Nonetheless, blood pressure control rates remain suboptimal despite established treatment guidelines and effective pharmacologic therapies. In parallel, artificial intelligence (AI) has rapidly expanded within cardiovascular medicine, demonstrating promising capabilities in disease detection, risk prediction, and clinical decision support. However, most AI applications in hypertension have focused primarily on algorithmic performance rather than real-world implementation or measurable improvements in patient outcomes. This review examines artificial intelligence-driven hypertension management through the lens of quality improvement and prevention of end-organ damage. We summarize current applications of machine learning, deep learning, natural language processing, and imaging analytics in hypertension detection and risk stratification, and critically evaluate their integration into clinical workflows. Particular emphasis is placed on therapeutic inertia, primary care-centered implementation, and the use of AI to support continuous quality improvement frameworks. Beyond blood pressure reduction alone, we explore the potential of AI to identify patients at risk for hypertensive heart disease, heart failure, aortic pathology, renal dysfunction, and cerebrovascular events. We discuss implementation challenges, including external validation, algorithmic bias, workflow integration, and regulatory considerations, which must be addressed to ensure safe and equitable deployment. Artificial intelligence offers the opportunity to transform hypertension management from reactive blood pressure control to proactive organ protection. Critically, AI-driven quality improvement interventions must be evaluated against established non-AI strategies, including pharmacist-led management and team-based care, which provide the benchmarks for demonstrating added clinical value. Achieving this shift will require embedding predictive analytics within structured, outcome-oriented systems of care and rigorously evaluating their impact on cardiovascular morbidity and mortality.<\/jats:p>","DOI":"10.3390\/life16040573","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T14:09:54Z","timestamp":1775138994000},"page":"573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence\u2013Driven Hypertension Management: Implications for Quality Improvement and Prevention of End-Organ Damage"],"prefix":"10.3390","volume":"16","author":[{"given":"Laura","family":"Ramlawi","sequence":"first","affiliation":[{"name":"Department of Science, Marianopolis College, Westmount, QC H3Y 1X9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1822-768X","authenticated-orcid":false,"given":"Serge","family":"Sicouri","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery Research, Lankenau Institute for Medical Research, Main Line Health, Wynnewood, PA 19096, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasiliki","family":"Androutsopoulou","sequence":"additional","affiliation":[{"name":"Department of Cardiothoracic Surgery, University of Thessaly, Biopolis, 41110 Larissa, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3754-6704","authenticated-orcid":false,"given":"Massimo","family":"Baudo","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery Research, Lankenau Institute for Medical Research, Main Line Health, Wynnewood, PA 19096, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9439-3946","authenticated-orcid":false,"given":"Andrew","family":"Xanthopoulos","sequence":"additional","affiliation":[{"name":"Department of Cardiology, University of Thessaly, Biopolis, 41110 Larissa, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1143-2293","authenticated-orcid":false,"given":"Alexandra","family":"Bekiaridou","sequence":"additional","affiliation":[{"name":"Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, NY 11030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5417-6392","authenticated-orcid":false,"given":"Dimitrios E.","family":"Magouliotis","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery Research, Lankenau Institute for Medical Research, Main Line Health, Wynnewood, PA 19096, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"ref_1","unstructured":"GBD 2019 Risk Factors Collaborators (2020). 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