ArticleCureus2026
Artificial Intelligence in Hypertension Management: Promise Must Precede Practice.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypertension remains one of the most important modifiable risk factors for cardiovascular disease worldwide, yet blood pressure control rates remain suboptimal despite advances in diagnosis and treatment. Artificial intelligence (AI) has emerged as a promising tool to improve hypertension care through enhanced risk prediction, continuous monitoring, and clinical decision support. Enthusiasm surrounding AI must be balanced against challenges related to algorithmic bias, model interpretability, data quality, and clinical implementation. While AI has the potential to transform hypertension management, its greatest challenge is no longer predictive accuracy but successful integration into real-world clinical practice. This editorial discusses the opportunities and limitations of AI in hypertension care and argues that implementation science, transparency, and equitable deployment should become the next priorities for the field.
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.