Evidence map›Paper›PMID 42792352›Full record

ReviewBehavioral sciences (Basel, Switzerland)2026

A Meta-Analysis of the Acceptance of Artificial Intelligence in Psychotherapy.

Meng Fan, Xiaojing Xie, Tiansheng Xia

Abstract readReview
In one paragraph

Review in Behavioral sciences (Basel, Switzerland), 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
–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

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

3 authors.

Meng FanMental Health Education Center, Guangdong University of Technology, Guangzhou 510006, China.ORCID 0009-0007-5529-3436
Xiaojing XieSchool of Art & Design, Guangdong University of Technology, Guangzhou 510090, China.ORCID 0009-0001-4275-1561
Tiansheng XiaSchool of Art & Design, Guangdong University of Technology, Guangzhou 510090, China.ORCID 0000-0002-6943-2958

Funding

Guangdong Provincial 2024 Educational Science Planning Project (Moral Education Special Pro-ject) 2024JKDYO16uangdong Provincial University Ideological and Political Education Project 2025GXSZ023
6 · The paper itself

Abstract

In recent years, artificial intelligence (AI) technology has demonstrated potential in psychotherapy. However, there is currently a lack of systematic research that comprehensively assessed public acceptance of AI-based psychotherapy. The purpose of this study is to use meta-analysis to explore public acceptance of AI in psychotherapy and the sources of heterogeneity. A random-effects model was employed, using Hedges' g as the effect size, to pool the differences in acceptance between the AI group and the conventional psychotherapy group. Subgroup analysis and meta-regression were used to explore the sources of heterogeneity. The review included a total of 10 studies involving 1967 participants. Meta-analysis revealed no statistically significant overall difference in acceptance between AI-based psychotherapy and conventional therapy; however, substantial heterogeneity was observed across studies, indicating that this pooled estimate should be interpreted cautiously. Subgroup analysis suggested that the type of conventional psychotherapy, cultural background, and AI type may contribute to variations in acceptance; however, these findings should be interpreted cautiously due to the limited number of studies in some subgroups. Meta-regression analysis indicated that studies with later publication dates and larger sample sizes reported relatively lower AI acceptance. Public acceptance of AI-based psychotherapy may be influenced by multiple factors, including AI type, cultural background, and characteristics of comparison conditions. Future research should focus on cultural differences, the professionalism of AI tools, and the quality of research methodology to more clearly define the appropriate contexts for AI in psychotherapy.

Indexed as

acceptanceartificial intelligenceheterogeneitymeta-analysispsychotherapy

Identifiers

PMID42792352
PMCPMC13603263

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.