Evidence mapPaperPMID 41370787Full record

ArticleJMIR mental health2025

Evaluating Generative AI Psychotherapy Chatbots Used by Youth: Cross-Sectional Study.

Kunmi Sobowale, Daniel Kevin Humphrey, Sophia Yingruo Zhao

Abstract read
In one paragraph

Article in JMIR mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  3. Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026
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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.

Kunmi SobowaleDepartment of Psychiatry and Biobehavioral Sciences, University of California, 760 Westwood Plaza, Suite 48-241, Los Angeles, CA, 90024, United States, 1 310-794-7035, 1 925-281-3270.ORCID http://orcid.org/0000-0002-3489-7114
Daniel Kevin HumphreyDepartment of Psychology, College of Arts and Science, University of San Francisco, San Francisco, CA, United States.ORCID http://orcid.org/0009-0005-3530-4279
Sophia Yingruo ZhaoUniversity of California Los Angeles, Los Angeles, CA, United States.ORCID http://orcid.org/0009-0009-4937-7443

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Many youth rely on direct-to-consumer generative artificial intelligence (GenAI) chatbots for mental health support, yet the quality of the psychotherapeutic capabilities of these chatbots is understudied. Objective: This study aimed to comprehensively evaluate and compare the quality of widely used GenAI chatbots with psychotherapeutic capabilities using the Conversational Agent for Psychotherapy Evaluation II (CAPE-II) framework. Methods: In this cross-sectional study, trained raters used the CAPE-II framework to rate the quality of 5 chatbots from GenAI platforms widely used by youth. Trained raters role-played as youth using personas of youth with mental health challenges to prompt chatbots, facilitating conversations. Chatbot responses were generated from August to October 2024. The primary outcomes were rated scores in 9 sections. The proportion of high-quality ratings (binary rating of 1) across each section was compared between chatbots using Bonferroni-corrected chi-square tests. Results: While GenAI chatbots were found to be accessible (104/120 high-quality ratings, 86.7%) and avoid harmful statements and misinformation (71/80, 89%), they performed poorly in their therapeutic approach (14/45, 31%) and their ability to monitor and assess risk (31/80, 39%). Privacy policies were difficult to understand, and information on chatbot model training and knowledge was unavailable, resulting in low scores. Bonferroni-corrected chi-square tests showed statistically significant differences in chatbot quality in the background, therapeutic approach, and monitoring and risk evaluation sections. Qualitatively, raters perceived most chatbots as having strong conversational abilities but found them plagued by various issues, including fabricated content and poor handling of crisis situations. Conclusions: Direct-to-consumer GenAI chatbots are unsafe for the millions of youth who use them. While they demonstrate strengths in accessibility and conversational capabilities, they pose unacceptable risks through improper crisis handling and a lack of transparency regarding privacy and model training. Immediate reforms, including the use of standardized audits of quality, such as the CAPE-II framework, are needed. These findings provide actionable targets for platforms, regulators, and policymakers to protect youth seeking mental health support.

Indexed as

Artificial IntelligenceCommunicationMental DisordersPsychotherapyAdolescentCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMaleartificial intelligencechatbotsChatGPTconversational agentdigital health: therapyevaluation frameworkgenerative AIlarge language modelspsychotherapypsychotherapy chatbots

Identifiers

PMID41370787
PMCPMC12694945

What Socratic holds

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LicenceCC BY
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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 Socratic graph.