Abstract
Background and Objective: The integration of artificial intelligence (AI) in healthcare has revolutionized the management of various diseases, including hypertension. This review explores the potential of AI applications in improving hypertension diagnosis, treatment, and patient outcomes. Methods: A comprehensive literature review was conducted using databases such as PubMed, Scopus, and IEEE Xplore. Studies published between 2010 and 2023 were included, focusing on AI models used for hypertension management, such as machine learning algorithms and predictive analytics. Results: AI has demonstrated significant accuracy in predicting hypertension risk factors, optimizing treatment plans, and monitoring patient adherence. Machine learning models, such as neural networks and decision trees, have shown up to 95% accuracy in early hypertension detection. Furthermore, AI-driven personalized treatment plans have resulted in improved patient outcomes and reduced healthcare costs. Conclusions: AI holds tremendous potential in transforming hypertension management by enhancing diagnostic precision and personalizing treatment strategies. Future research should focus on integrating AI with electronic health records and developing real-time monitoring systems to further improve patient care.
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