Previously submitted to: JMIR Mental Health (no longer under consideration since Jul 17, 2025)
Date Submitted: Jul 16, 2025
Open Peer Review Period: Jul 17, 2025 - Jul 17, 2025
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Evaluating adolescent depression-related GAI agents on the basis of the FAITA-Mental Health Framework: A Comparative Case Series Study
ABSTRACT
Background:
Adolescent depression is a pressing global health concern, with timely screening vital for early intervention. Barriers like limited access to professional resources, stigma, and adolescent reluctance often hinder early detection. Generative artificial intelligence (GAI) tools offer scalable, low-stigma support but remain exploratory for depression diagnosis, with limited evidence on reliability, usability, and alignment with established clinical standards—especially for adolescents, whose unique needs demand tailored assessments.
Objective:
This study aimed to evaluate the performance and consistency of a GAI-based robot named “Xiao-Yu” for adolescent depression screening, using the FAITA-Mental Health framework. It sought to assess the tool’s usability among adolescents with emotional disorders, compare its diagnostic accuracy against psychiatric professionals, and examine consistency via test-retest consistency after treatment. Additionally, it aimed to validate the FAITA-Mental Health framework’s applicability for evaluating GAI-driven mental health tools.
Methods:
Xiao-Yu was developed using the MINI-KID diagnostic scale, and GPT-4 was developed via prompt engineering. Its performance was compared with that of psychiatric professionals. Half of the participants underwent repeated testing with Xiao-Yu after two weeks of treatment. FAITA-Mental Health and supplemental metrics were used for evaluation.
Results:
Xiao-Yu achieved 100% task completion and a 72% F1 score in diagnosing 13 adolescents. The level of consistency with professionals was 77%. FAITA-Mental Health scores: trustworthiness, 6/6; user experience, 5/6; autonomy, 2/4; equity, 2/4; transparency, 1/2; crisis management, 2/2. User acceptance was 92%.
Conclusions:
The GAI has moderate diagnostic consistency, reliability, and high acceptability for adolescent depression. FAITA-Mental Health is a viable evaluation framework but should incorporate clinical metrics (e.g., accuracy, reliability) for practical utility.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.