Evidence map›Paper›PMID 42209800›Full record

ArticleNPJ digital medicine2026

Effectiveness of AI and rule-based conversational agents for depression, anxiety and stress: A meta-analysis.

Saeed Mokhtari Masoumi Alamdarloo, Ali Mirzakhani, Mohamad Azhdarloo, Elaheh Haghani-Samani, Abouzar Nazari

Abstract read
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Saeed Mokhtari Masoumi AlamdarlooDepartment of Clinical Psychology, Faculty of Educational Sciences and Psychology, Shiraz University, Shiraz, Iran.ORCID http://orcid.org/0000-0003-4298-8267
Ali MirzakhaniDepartment of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine (SATiM), Tehran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0009-0002-5056-2792
Mohamad AzhdarlooMA, Department of Psychology, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.ORCID http://orcid.org/0000-0002-5461-7511
Elaheh Haghani-SamaniDepartment of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine (SATiM), Tehran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0009-0006-2372-0117
Abouzar NazariDepartment of Public Health, Borujen Faculty of Medical Sciences, Shahrekord University of Medical Sciences, Shahrekord, Iran. abozarnazari368@gmail.com.ORCID http://orcid.org/0000-0003-2155-5438

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Depression, anxiety, and stress are significant global health burdens worsened by restricted access to care. Conversational agents (CAs), encompassing both AI(Artificial Intelligence)- and rule-based systems, provide scalable mental health interventions. This systematic review and meta-analysis analyzed the efficacy of CA interventions in 48 randomized controlled trials covering 28,071 participants. It demonstrated small-to-moderate but significant effects in minimizing symptoms of depression (SMD: -0.27), anxiety (SMD: -0.20), and stress (SMD: -0.26). A Robust Variance Estimation (RVE) approach was used to address within-study dependencies, with results consistent with the random-effects model. Subgroup analyses investigated sources of heterogeneity and indicated greater effects in clinical populations and shorter-duration interventions. Meta-regression identified no appreciable effect of outcome timing. Risk of bias was largely low, although some studies had issues due to missing data or selective reporting. Publication bias was negligible. These results provide evidence for the utilization of conversational agents as effective interventions for lessening psychological symptoms. Additional research is required to evaluate their long-term effectiveness and how best to integrate them into mental healthcare systems.

Identifiers

PMID42209800
PMCPMC13503911

What OpenQuestion holds

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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.