{"title":"Artificial Intelligence to Improve Blood Pressure Control: A State-of-the-Art Review.","authors":"Amogh Karnik, Eugene Yang","doi":"10.1093/ajh/hpaf035","DOIUrl":null,"url":null,"abstract":"<p><p>Hypertension remains a major global health challenge, contributing to significant morbidity and mortality. Advances in artificial intelligence (AI) and machine learning (ML) are transforming hypertension care by enhancing blood pressure (BP) measurement, risk assessment, and personalized treatment. AI-powered technologies have the potential to enable accurate non-invasive BP monitoring and facilitate tailored lifestyle modifications, enhancing adherence and outcomes. ML models can also predict hypertension risk based on demographic, lifestyle, and clinical data, enabling earlier intervention and prevention strategies. However, challenges such as the lack of standardized validation protocols and potential biases in AI systems may widen health disparities. Future research must prioritize rigorous validation across diverse populations and ensure algorithm transparency. By leveraging AI responsibly, we can revolutionize hypertension management, enhance health equity, and improve cardiovascular outcomes.</p>","PeriodicalId":7578,"journal":{"name":"American Journal of Hypertension","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Hypertension","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/ajh/hpaf035","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PERIPHERAL VASCULAR DISEASE","Score":null,"Total":0}
引用次数: 0
Abstract
Hypertension remains a major global health challenge, contributing to significant morbidity and mortality. Advances in artificial intelligence (AI) and machine learning (ML) are transforming hypertension care by enhancing blood pressure (BP) measurement, risk assessment, and personalized treatment. AI-powered technologies have the potential to enable accurate non-invasive BP monitoring and facilitate tailored lifestyle modifications, enhancing adherence and outcomes. ML models can also predict hypertension risk based on demographic, lifestyle, and clinical data, enabling earlier intervention and prevention strategies. However, challenges such as the lack of standardized validation protocols and potential biases in AI systems may widen health disparities. Future research must prioritize rigorous validation across diverse populations and ensure algorithm transparency. By leveraging AI responsibly, we can revolutionize hypertension management, enhance health equity, and improve cardiovascular outcomes.
期刊介绍:
The American Journal of Hypertension is a monthly, peer-reviewed journal that provides a forum for scientific inquiry of the highest standards in the field of hypertension and related cardiovascular disease. The journal publishes high-quality original research and review articles on basic sciences, molecular biology, clinical and experimental hypertension, cardiology, epidemiology, pediatric hypertension, endocrinology, neurophysiology, and nephrology.