Key result
Artificial intelligence holds transformative potential for personalized diagnosis and management of hypertension, though large-scale clinical adoption currently requires further evidence generation.
Why the study?
Despite the potential of artificial intelligence to advance precision hypertension, its clinical adoption at scale is lacking and clinical implementation needs evidence generation.
Artificial intelligence offers transformative potential for personalized hypertension care, but requires further evidence generation for large-scale clinical implementation.
Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence-based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.
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Armoundas et al. (2025) conducted a review in Hypertension. Artificial Intelligence was evaluated. Artificial intelligence holds transformative potential for personalized diagnosis and management of hypertension, though large-scale clinical adoption currently requires further evidence generation.
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