Previously submitted to: JMIR Mental Health (no longer under consideration since Nov 07, 2025)
Date Submitted: Nov 7, 2025
Open Peer Review Period: Nov 7, 2025 - Nov 7, 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 Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).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Warning: This is an author submission that is not peer-reviewed or edited.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.
A Bibliometric Analysis of Large Language Models in Mental Health Research
ABSTRACT
Background:
Mental health disorders constitute a critical global health challenge, accounting for nearly 30% of the worldwide non-fatal disease burden and resulting in annual productivity losses exceeding $1 trillion. Despite increased awareness and investment, significant barriers,such as limited access to care, societal stigma and a shortage of trained professionals,continue to obstruct effective service delivery. Recently, Large Language Models have emerged as transformative tools in mental health research and practice, offering potential applications in diagnostics, therapeutic support, and patient engagement. However, the field remains fragmented, with critical challenges in areas such as data quality, ethical governance and the lack of standardized evaluation frameworks.
Objective:
This study aims to provide a comprehensive bibliometric analysis of research on Large Language Models in mental health between 2020 and 2025. It seeks to quantify publication trends to analyse the growth and scholarly interest in the field, identify influential contributors such as key authors, institutions and funding bodies, and analyse global collaboration networks to uncover disparities and opportunities for inclusive partnerships. Furthermore, the study maps the thematic and conceptual evolution of the field, tracking emerging topics like chatbots, multilingual models and therapeutic applications while highlighting shifts in research priorities.
Methods:
To ensure comprehensive coverage of the literature, searches were conducted across seven major sources: Web of Science, Scopus, PubMed, Dimensions, OpenAlex, The Lens, and the Cochrane Library. The combined datasets comprised of 472 articles which provided a foundation for subsequent analyses. A bibliometric approach was then employed to analyse the collected data, focusing on publication trends, collaboration networks and thematic evolution within the field.
Results:
The analysis revealed a rapid acceleration in scholarly output, with a compound annual growth rate of 92.8%, driven by advancements in models such as GPT-3 and GPT-4, alongside strategic funding and industry initiatives. The geographic distribution of research demonstrated a significant imbalance, with high-income countries, particularly the United States, dominating the field, while low- and middle-income countries were underrepresented. Thematic evolution showed a transition from foundational research (2020–2022) to applied studies (2023–2025), with emerging topics including chatbots, multilingual models, and therapeutic applications. Despite these advancements, critical gaps persist in clinical translation, ethical oversight, and equitable access. Collaboration network analysis highlighted the dominance of well-resourced institutions and limited cross-regional partnerships, especially those involving LMICs.
Conclusions:
The findings underscore the need for more inclusive and globally integrated research efforts to fully realise the potential of Large Language Models in mental health care. Future research must prioritise equity, strengthen pathways for clinical translation and develop robust ethical and evaluation frameworks. Policymakers, funders, and clinicians must champion interdisciplinary, ethically guided research to ensure the safe, effective and socially responsible integration of Large Language Models into mental health systems. Clinical Trial: None applicable
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.