Barriers to understanding how many people use AI for mental health support: an estimate and narrative review
Open accessAbstract
Reports suggest 3–70% of AI users utilize AI for mental health. A more precise estimate is needed to guide public health policy, research, and to understand the limitations of producing an estimate. We approximate that 27% of AI users utilize AI for mental health support. However, we acknowledge that heterogeneous data sources and a lack of nosology standardization confound estimates. Consensus building will be key before better prevalence estimates are possible.
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People increasingly bring mental-health concerns to general-purpose LLMs, often in moments of acute distress and outside any clinical pathway.
“People increasingly bring mental-health concerns to general-purpose LLMs, often in moments of acute distress and outside any clinical pathway (Bodner et al., 2026;Rousmaniere et al., 2026).”
From HealthBench-Psych: A Mental Health Subset of OpenAI's HealthBench (2026) · cites this paper as Bodner et al., 2026
The opening discussion reports increased interest in AI for mental health support, and the Additional studies section describes emotional conversations with Claude and ChatGPT. The paper does not establish that people often use these tools in moments of acute distress or outside a clinical pathway; it also says precise use patterns remain unclear. No page numbers are available in our copy. (LLM, conf=0.85)
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Cites
Chatterji, A., Cunningham, T., Deming, D., Hitzig, Z., Ong, C., Shan, C. Y., & Wadman, K. (2025) · doi:10.3386/w34255 · Cited by 233 · see citing works
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Research works printed but not linked: 7
- Zao-Sanders, M. How People are Really Using Generative AI Now. Harvard Business Review. (2025).
- Siddals, S., Torous, J. & Coxon, A. It happened to be the perfect thing: experiences of generative AI chatbots for mental health. npj Ment. Health Res 3, 48 (2024).
- McBain R. K. et al. Use of Generative AI for Mental Health Advice Among US Adolescents and Young Adults. JAMA Netw Open. 2025. https://doi.org/10.1001/jamanetworkopen.2025.42281. · DOI
- Albikawi, Z., Abuadas, M. & Rayani, A. M. Nursing Students' Perceptions of AI-Driven Mental Health Support and Its Relationship with Anxiety, Depression, and Seeking Professional Psychological Help: Transitioning from Traditional Counseling to Digital Support. Healthcare 13, 1089 (2025).
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- Rousmaniere, T., Zhang, Y., Li, X. & Shah, S. Large language models as mental health resources: Patterns of use in the United States. Practice Innovations. Advance online publication. https://doi.org/10. 1037/pri0000292 (2025). · DOI
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HealthBench-Psych: A Mental Health Subset of OpenAI's HealthBench
Flathers, M., Nguyen, P. A., Noorily, J., Herpertz, J., Chen, M., Multani, J., Powell, S., Granof, M., Kalinch, M., & Torous, J. (2026) · arXiv · doi:10.48550/arxiv.2608.25071
“People increasingly bring mental-health concerns to general-purpose LLMs, often in moments of acute distress and outside any clinical pathway (Bodner et al., 2026; Rousmaniere et al., 2026).”
Self-citation
- Authors
- Bodner, R., Lim, K., Siddals, S., Goldberg, S., & Torous, J.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- University of Wisconsin–Madison (US)
- Corresponding author
- Torous, John
- Venue
- npj Digital Public Health
- Published
- 2026-07-03
- Volume
- 1
- Issue
- 1
- ISSN
- 3091-2784 (ISSN-L)
- OpenAlex
- W7167219892
- Cited by
- 5
- References
- 1
- Citations by year
- 2026: 5
- FWCI
- 4.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- npj Digital Public Health (publishedVersion, cc-by-nc-nd)
- Funders
- Argosy Foundation
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, mental health support, prevalence estimation, artificial intelligence use, heterogeneous data sources, public health policy
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