Clinical applications of artificial intelligence in hypertension management: current evidence and future perspectives

Hypertension remains the leading modifiable risk factor for cardiovascular morbidity and mortality worldwide, with persistently inadequate blood pressure control despite guideline-directed therapy. The rapid expansion of digital health data and computational capacity has positioned artificial intelligence (AI) as a promising tool for improving hypertension management through enhanced risk prediction, phenotyping, and individualized care. However, important challenges related to external validation, interpretability, implementation, and real-world clinical benefit remain unresolved.

We conducted a structured narrative review with a systematic literature search across PubMed/MEDLINE, Embase, and Scopus for studies published between January 2015 and December 2025. Eligible studies evaluated clinically relevant applications of AI in hypertension, including screening, diagnosis, cardiovascular risk stratification, treatment optimization, clinical decision support, and remote monitoring. Findings were synthesized qualitatively because of substantial heterogeneity in study design, AI methodology, and reported outcomes.

AI-based models demonstrated favorable performance in predicting incident hypertension and cardiovascular risk using electronic health records, wearable technologies, and multimodal clinical datasets. Machine learning approaches frequently outperformed conventional risk prediction models, with reported area under the curve values generally ranging from approximately 0.75 to 0.90 across representative studies. AI-supported systems also showed potential for personalized antihypertensive therapy, resistant hypertension identification, and continuous blood pressure monitoring. However, most available evidence remains based on retrospective or internally validated datasets, and relatively few studies have demonstrated robust external validation or improvements in hard clinical outcomes such as cardiovascular events or mortality.

Major barriers to implementation include data heterogeneity, algorithmic bias, limited interpretability, insufficient external validation, infrastructure and cost requirements, regulatory uncertainty, and concerns regarding patient trust and data privacy. In addition, evidence supporting widespread clinical implementation remains limited by the scarcity of large prospective randomized trials.

AI has the potential to substantially transform hypertension management by enabling more precise, proactive, and personalized care. Nevertheless, rigorous prospective validation, improved transparency, equitable implementation strategies, and seamless integration into clinical workflows will be essential before widespread clinical adoption can be achieved.