Evidence map›Paper›PMID 38956284›Full record

SynthesisHypertension research : official journal of the Japanese Society of Hypertension2024

Does clinical practice supported by artificial intelligence improve hypertension care management? A pilot systematic review.

Toshiki Maeda, Yuki Sakamoto, Satoshi Hosoki, Atsushi Satoh, Rie Koyoshi, Sumiyo Yamashita, Hisatomi Arima

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Hypertension research : official journal of the Japanese Society of Hypertension, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. The Japanese Society of Hypertension Guidelines for blood pressure control using digital technologies.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
    Article
  3. Advances in digital technology in healthcare.Hypertension research : official journal of the Japanese Society of Hypertension · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Toshiki MaedaDepartment of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, Fukuoka, Japan. tmaeda@fukuoka-u.ac.jp.
Yuki SakamotoDepartment of Neurology, Nippon Medical School, Tokyo, Japan.
Satoshi HosokiCentre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.
Atsushi SatohDepartment of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, Fukuoka, Japan.
Rie KoyoshiDivision of Medical Safety Management, Fukuoka University Hospital, Fukuoka, Japan.
Sumiyo YamashitaDepartment of Cardiology, Nagoya City University Mirai Kousei Hospital, Nagoya, Japan.
Hisatomi ArimaDepartment of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, Fukuoka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although artificial intelligence (AI) is considered to be a promising tool, evidence for the effectiveness of AI-supported clinical practice for lowering blood pressure (BP) in the real world is scarce. We conducted a systematic review to elucidate whether AI-supported clinical care improves BP control. We identified two randomized control trials (RCTs) in a literature search. The results revealed no significant difference between AI-supported care and usual care in a random-effects model meta-analysis of RCTs (AI vs. usual care: systolic/diastolic BP difference: -2.13 [95% confidence interval: -4.72 to 0.46] / -1.03 [-2.52 to 0.46]). In this review, we were unable to clarify whether AI-supported clinical practice improved BP control compared with usual care. Further studies will be needed to provide robust evidence for the effectiveness of AI-supported care in clinical settings.

Indexed as

Artificial IntelligenceHypertensionBlood PressureHumansPilot ProjectsRandomized Controlled Trials as TopicArtificial intelligenceDigital technologyMachine learning

Identifiers

PMID38956284

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.