Aug 2026· npj Digital Medicine· Vol 9· 2 citations· 67 references
Medicine
Abstract
The extent to which people use general-purpose large language models (LLMs) for their mental health is unknown. Information about use patterns is important for clinicians, developers, and regulators. We surveyed U.S. adults (n = 1871) between August and October 2024 using stratified sampling across age, sex, and race/ethnicity to approximate national demographics. We found that 24% of participants use LLMs for mental health; they are disproportionately young, male, and Black, and have poor mental health. Participants reported difficulty accessing traditional treatment and using LLMs because they are free, convenient, and available. They report using LLMs for emotional support, learning therapy skills, and supplementing existing therapy. Using Pew-reported estimates of population LLM use, we conservatively estimate that as of 2024, 14–18 million U.S. adults may have been using LLMs for mental health. This work highlights the need for monitoring and evaluation to understand the potential harms and benefits of such use.
Large language models are best understood as emerging assessment-support tools rather than replacements for clinical evaluation because the limited pace of academic validation means that, at present, LLMs are best understood as emerging assessment-support tools rather than replacements for clinical evaluation.
Katie Aafjes-van Doorn, Francine Cheng Ty, A. Hua et al.· Journal of Psychopathology a...· 0 citations
Current performance estimates of LLMs with respect to depression screening are most likely optimistic, but when restricted to smaller models that could be locally deployed (for privacy protection) in a clinical setting, LLMs do not detect depression with sufficient accuracy, sensitivity, or specificity to be used in a...
Sing-Hui Ling, W. Chorney· Acta Psychologica· 0 citations
GenAI chatbot use for mental health support has become commonplace among Canadian university students and is concentrated among those with greater mental health needs and fewer social support resources.
L. Olisaeloka, R. Munthali, D. Vigo· medRxiv· 0 citations
Abstract Background A growing body of literature leverages large language models (LLMs) to make mental health predictions. However, these models are prone to bias, and studies to validate their clinical utility are lacking. Objective This scoping review aims to uncover bias and clinical utility limitations stemming fro...
Clémentine Bleuze, Karen Fort, Vincent P. Martin et al.· JMIR AI· 0 citations
Large language models (LLMs) are rapidly becoming embedded in everyday mental health help-seeking practices, particularly among young people who already turn to digital platforms as gateways to mental health support. While LLMs offer unprecedented immediacy and accessibility, their integration into help-seeking ecosyst...
Claudette Pretorius· Information Hiding· 0 citations
This editorial updates the research priorities articulated by JMIR Mental Health in 2023, while reaffirming their emphasis on equity, replicability, privacy, efficacy, and engagement.
M. Birk, Shruti Kochhar, Keris Myrick et al.· JMIR Mental Health· 1 citation
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