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41/100

✓ Peer-reviewed alzhub usefulness score 41 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

PURPOSE OF REVIEW: Hypertension 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 FINDINGS: Using 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.

Key findings

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.

Why it matters

This review covers AI used in diagnosing, monitoring, and predicting outcomes in hypertension, which is described as a modifiable risk factor for dementia. It does not test any treatment, cure, or prevention for Alzheimer's disease itself.

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Study details

Evidence strengthLow

How this study scored 41/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Current Opinion in Nephrology & Hypertension)
Journal's citation record	1 / 4	Current Opinion in Nephrology & Hypertension: journal h-index 125 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	5 / 5	2026 — within 1 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	1 / 7	Cited 3 times (OpenAlex, 2026-10-10)
Citations per year	1 / 5	1.5 citations a year over 2 year(s) since 2026
Impact for its field and year	4 / 6	Cited 14.5× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	1 / 4	Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Eduard Shantsila: h-index 45 (OpenAlex, whole career)
Institution's track record	2 / 3	University College London: institution h-index 2,029 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 4 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41491933)
Free to read	2 / 2	Free to read
Total	41 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Dherani, M., Wagner, S. K., & Shantsila, E. (2026). The role of artificial intelligence in hypertension management. Current Opinion in Nephrology & Hypertension. https://doi.org/10.1097/mnh.0000000000001157

Vancouver

Dherani M, Wagner SK, Shantsila E. The role of artificial intelligence in hypertension management. Current Opinion in Nephrology & Hypertension. 2026. doi:10.1097/mnh.0000000000001157. PMID: 41491933.

BibTeX

@article{dherani2026-41491933,
  author  = {Dherani, Mukesh and Wagner, Siegfried K. and Shantsila, Eduard},
  title   = {{The role of artificial intelligence in hypertension management}},
  journal = {Current Opinion in Nephrology \& Hypertension},
  year    = {2026},
  doi     = {10.1097/mnh.0000000000001157},
  pmid    = {41491933},
  url     = {https://doi.org/10.1097/mnh.0000000000001157}
}

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