ReviewCurrent opinion in nephrology and hypertension2026
The role of artificial intelligence in hypertension management.
Review in Current opinion in nephrology and hypertension, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Application of digital health technologies in hypertension self-management: a narrative review.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewHypertension remains a leading modifiable risk factor for cardiovascular and renal conditions and dementia. Given its rising global prevalence and economic burden, artificial intelligence offers promising solutions across the care continuum, from diagnosis to monitoring. This review highlights recent advances in artificial intelligence-driven diagnosis and monitoring, risk stratification, and predictive modelling of hypertension-related outcomes. RECENT
findingsUsing artificial intelligence-based technologies, validated wearable cuffless monitors are developed, which use electrocardiography, heart sounds, and thoracic impedance data and provide continuous blood pressure (BP) monitoring. Artificial intelligence-generated algorithm have shown promising response to accurately predict BP. The Extreme Gradient Boost has consistently performed as the best algorithms. Additionally, these models have been used in predicting hypertension impact on cardiovascular, renal, and retinal conditions, and in predicting treatment strategies. Emerging applications of Large Language Models are being developed to provide personalized care based on individual patient characteristics. SUMMARY: Artificial intelligence has the potential to transform hypertension management through improved diagnosis, monitoring, and personalized care and prediction of its systemic consequences. However, challenges of model validation, interpretability, generalizability, and ethics persist. Robust prospective trials and equitable implementation strategies can help realise the potential of artificial intelligence in improving hypertension outcomes.
Indexed as
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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.