ReviewHypertension (Dallas, Tex. : 1979)2025
Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.
Review in Hypertension (Dallas, Tex. : 1979), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Endocrine-Disrupting Chemicals and Hypertension: Mechanisms and Clinical Implications.Journal of cardiovascular development and disease · 2026Review
- Review
- Editorial for the Special Issue "Diabetes, Hypertension, and Cardiovascular Diseases: New Insights, Risk Factors, and Drug Therapies".Medicina (Kaunas, Lithuania) · 2026Article
- The effects of multitype prompt engineering for large language models in hypertension treatment decisions.NPJ digital medicine · 2026Article
- Measuring blood pressure accurately.Australian prescriber · 2026Review
- Digital hypertension in 2024-2025: emerging evidence and future directions.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Review
- Evolution in the targets for blood pressure treatment.Current opinion in nephrology and hypertension · 2026Review
- The role of artificial intelligence in hypertension management.Current opinion in nephrology and hypertension · 2026Review
- Implementation hypertension: a new paradigm for global hypertension control in the JSH 2025, WHO 2025, and AHA/ACC 2025 guideline era.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Development and validation of a machine learning model to predict comorbid hypertension in patients with type 2 diabetes.Frontiers in medicine · 2026Article
- Explainable Machine Learning for Risk Prediction of Reduced Quality of Life in Hypertension.Vascular health and risk management · 2026Article
- Smart Community Hypertension Management: A Narrative Review of Promises and Challenges.Journal of multidisciplinary healthcare · 2026Review
- Arterial stiffness and vascular aging: mechanisms, prevention, and therapy.Signal transduction and targeted therapy · 2025Review
- Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management.Hypertension (Dallas, Tex. : 1979) · 2025Review
- Navigating the 2024 ESC Hypertension Guidelines: What Is New, Context, and Future Directions.Journal of the American College of Cardiology · 2025Article
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors.
Funding
Abstract
Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.