Evidence map›Paper›PMID 42461826›Full record

ArticlePLOS digital health2026

Artificial intelligence-based chatbots to enhance medication adherence among patients with non-communicable chronic diseases: Systematic review and meta-analysis.

Siyu Chen, Yuan Fang, Liwen Ding, Phoenix K H Mo, Zixin Wang

Abstract read
In one paragraph

Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Siyu ChenCentre for Health Behaviours Research, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China.ORCID https://orcid.org/0000-0002-6271-1858
Yuan FangCentre for Health Behaviours Research, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China.
Liwen DingCentre for Health Behaviours Research, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China.
Phoenix K H MoCentre for Health Behaviours Research, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China.
Zixin WangCentre for Health Behaviours Research, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China.ORCID https://orcid.org/0000-0002-1158-2304

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medication adherence remains a major public health challenge among patients with non-communicable diseases (NCDs) worldwide. Artificial intelligence (AI)-based chatbots may enhance adherence by automating reminders, education, and real-time support. This systematic review and meta-analysis evaluated the effectiveness of AI-based chatbots in improving medication adherence among patients with NCDs. This review (CRD420251151031) is reported in accordance with the PRISMA guidelines. Relevant studies were identified from PubMed, MEDLINE, Embase, Web of Science, Global Health, CINAHL, Cochrane Library, APA PsycINFO, and APA PsycArticles up to August 2025. Eligible designs included randomized controlled trials (RCTs), quasi-experimental studies, and single-arm pre-post studies. Seven studies published between 2017 and 2025 were included, of which six RCTs contributed to the meta-analysis. The pooled standardized mean difference (SMD) for medication adherence was 0.69 (95% confidence interval: 0.17 to 1.22; p = 0.01), indicating a significant medium effect with high heterogeneity (I2 = 97%). Subgroup analyses revealed greater effects for cardiovascular diseases and short-term interventions (<6 months). Daily and weekly to monthly interventions were effective, whereas on-demand or episodic interventions were not. Significant improvements were also observed when adherence was assessed using self-reported measures or pill counts. AI-based chatbots that incorporated three functions (medication reminders, education and health coaching, and real-time question-answer features) showed significant improvement in medication adherence, whereas those with dual functions did not. These findings indicate that AI-based chatbots significantly improve medication adherence in NCDs. Chatbots have the potential to supplement existing interventions to support medication adherence.

Identifiers

PMID42461826
PMCPMC13375028

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Registered trials

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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.