aceAI applications for hypertension management and cardiovascular risk prediction
Source article: 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, interpretabil…
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A structured narrative review of literature from January 2015 to December 2025 examined AI for hypertension screening, diagnosis, risk stratification, treatment optimization and remote monitoring. It found ML models often outperformed conventional risk scores with AUCs of 0.75 to 0.90 and showed promise for personalized therapy and continuous monitoring.
The clinical significance remains uncertain because most studies were retrospective without robust external validation, and few showed improvements in cardiovascular events or mortality. The authors conclude prospective validation, transparency, equitable implementation and workflow integration are needed before widespread adoption.
- Review searched PubMed/MEDLINE, Embase, Scopus for Jan 2015 to Dec 2025 studies on AI for screening, diagnosis, risk stratification, treatment optimization, decision support and remote monitoring.
- Machine learning approaches frequently outperformed conventional risk prediction models in included studies.
- AI-supported systems showed potential for personalized antihypertensive therapy, resistant hypertension identification, and continuous blood pressure monitoring.
AI models predicted incident hypertension and cardiovascular risk from EHRs, wearables and multimodal data with AUCs around 0.75 to 0.90, supporting personalized therapy and remote monitoring.
Most AI hypertension studies remain retrospective or internally validated, with few demonstrating external validation or gains in hard outcomes like cardiovascular events or mortality, plus barriers of bias, interpretability, and infrastructure.
The rundown
The review synthesized studies using EHRs, wearables and multimodal datasets for screening, diagnosis, risk stratification, treatment optimization and remote monitoring, noting heterogeneity prevented meta-analysis.
Authors flagged implementation barriers including data heterogeneity, algorithmic bias, limited interpretability, infrastructure and cost requirements, regulatory uncertainty, and patient trust and privacy concerns.
Evidence base is largely retrospective and internally validated, with few externally validated studies and scarce large prospective randomized trials showing hard outcomes.
Sources
- Peer-reviewedHerz2026-07-17
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