Evidence map›Paper›PMID 38114588›Full record

ReviewNPJ digital medicine2023

Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being.

Han Li, Renwen Zhang, Yi-Chieh Lee, Robert E Kraut, David C Mohr

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 227 papers, 23 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
227citing papers in PubMed, 23 pooled it
71.5field-weighted citation impact, top 1% of its field
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

227 citing papers in PubMed, 23 syntheses or guidelines pooled it, 363 citations in OpenAlex.

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  6. Responsible artificial intelligence integration framework for psychiatric guidelines.The international journal of neuropsychopharmacology · 2026
    Guideline
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167 more citing papers are in PubMed but not listed here.

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 at 3 institutions in 2 countries.

Han Li *Department of Communications and New Media, National University of Singapore, Singapore, 117416, Singapore.
Renwen Zhang *Department of Communications and New Media, National University of Singapore, Singapore, 117416, Singapore. r.zhang@nus.edu.sg.ORCID http://orcid.org/0000-0002-7636-9598
Yi-Chieh LeeDepartment of Computer Science, National University of Singapore, Singapore, 117416, Singapore.
Robert E KrautHuman-Computer Interaction Institute Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
David C MohrCenter for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611, USA.ORCID http://orcid.org/0000-0002-5443-7596
National University of Singapore · SGCarnegie Mellon University · USNorthwestern University · US

Funding

Technology Enabled Services for Coordinated Care of Depression in Healthcare SettingsP50MH119029 · NIMH · NORTHWESTERN UNIVERSITY AT CHICAGO · PI DAVID CURTIS MOHR, Jessica Lee Schleider · 2020 to 2026
$10.7M
NIMH NIH HHS P50 MH119029
6 · The paper itself

Abstract

Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience. Twelve databases were searched for experimental studies of AI-based CAs' effects on mental illnesses and psychological well-being published before May 26, 2023. Out of 7834 records, 35 eligible studies were identified for systematic review, out of which 15 randomized controlled trials were included for meta-analysis. The meta-analysis revealed that AI-based CAs significantly reduce symptoms of depression (Hedge's g 0.64 [95% CI 0.17-1.12]) and distress (Hedge's g 0.7 [95% CI 0.18-1.22]). These effects were more pronounced in CAs that are multimodal, generative AI-based, integrated with mobile/instant messaging apps, and targeting clinical/subclinical and elderly populations. However, CA-based interventions showed no significant improvement in overall psychological well-being (Hedge's g 0.32 [95% CI -0.13 to 0.78]). User experience with AI-based CAs was largely shaped by the quality of human-AI therapeutic relationships, content engagement, and effective communication. These findings underscore the potential of AI-based CAs in addressing mental health issues. Future research should investigate the underlying mechanisms of their effectiveness, assess long-term effects across various mental health outcomes, and evaluate the safe integration of large language models (LLMs) in mental health care.

Identifiers

PMID38114588
PMCPMC10730549
OpenAlexW4389934892

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.