Evidence map›Paper›PMID 42601385›Full record

ArticleNPJ digital medicine2026

Real-world use of large language models for mental health in 2024.

Elizabeth C Stade, Zoe M Tait, Samuel T Campione, Shannon Wiltsey Stirman, Johannes C Eichstaedt

Abstract read
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In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Elizabeth C StadeInstitute of Human-Centered Artificial Intelligence, Stanford University, Palo Alto, CA, USA. ecs@stanford.edu.
Zoe M TaitDepartment of Psychology, Stanford University, Palo Alto, CA, USA.
Samuel T CampioneInstitute of Human-Centered Artificial Intelligence, Stanford University, Palo Alto, CA, USA.
Shannon Wiltsey StirmanDepartment of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA.
Johannes C EichstaedtInstitute of Human-Centered Artificial Intelligence, Stanford University, Palo Alto, CA, USA. johannes.stanford@gmail.com.

Funding

National Institute of Mental Health, United States P50-MH139450NIMH NIH HHS R01-MH125702NIMH NIH HHS RF1-MH128785
6 · The paper itself

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.

Identifiers

What Socratic holds

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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.