Evidence map›Paper›PMID 41563755›Full record

ArticleJAMA network open2026

Generative AI Use and Depressive Symptoms Among US Adults.

Roy H Perlis, Faith M Gunning, Ata A Uslu, Mauricio Santillana, Matthew A Baum, James N Druckman, Katherine Ognyanova, David Lazer

Erratum issuedAbstract read
In one paragraph

Article in JAMA network open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Leveraging AI Tools to Express Gratitude.Technology, mind, and behavior · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Error in Byline.JAMA network open · 2026
    Article
  11. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Roy H PerlisCenter for Quantitative Health, Massachusetts General Hospital, Boston.
Faith M GunningDepartment of Psychiatry, Weill-Cornell Medical School, New York, New York.
Ata A UsluNetwork Science Institute, Northeastern University, Boston, Massachusetts.
Mauricio SantillanaNetwork Science Institute, Northeastern University, Boston, Massachusetts.
Matthew A BaumJohn F. Kennedy School of Government, Harvard University, Cambridge, Massachusetts.
James N DruckmanDepartment of Political Science, University of Rochester, Rochester, New York.
Katherine OgnyanovaDepartment of Communication, School of Communication and Information, Rutgers University, New Brunswick, New Jersey.
David LazerNetwork Science Institute, Northeastern University, Boston, Massachusetts.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Generative artificial intelligence (AI) has rapidly entered mainstream use in the US, but its association with mental health has not been characterized. Objective: To examine the associations of the extent and type of generative AI use among US adults with negative affective symptoms in a large, nationally representative sample. Design, Setting, and Participants: This survey study used data from a 50-state US internet nonprobability survey conducted between April and May 2025. Survey respondents were aged 18 years and older. Data were analyzed in August 2025. Exposure: Participants self-reported generative AI and social media use. Main Outcomes and Measures: The outcome of interest, negative affect, was measured using the Patient Health Questionnaire 9-item (PHQ-9). Results: There were 20 847 unique participants, with mean (SD) age 47.3 (17.1) years and 10 327 (49.5%) female, 10 386 (49.8%) male, and 134 (0.6%) nonbinary participants; 2152 participants (10.3%) reported using AI at least daily, including 1053 participants (5.1%) who reported daily use and 1099 participants (5.3%) who reported use multiple times per day. Among participants who used daily or more frequently, 1033 (48.0%) reported use for work, 246 (11.4%) for school, and 1875 (87.1%) for personal applications. In survey-weighted regression models, daily or more frequent AI use was significantly more common among men, younger adults, those with higher education and income, and those in urban settings. Greater AI use was associated with greater levels of depressive symptoms in sociodemographic-adjusted regression models: (daily use: β = 1.08 [95% CI, 0.55-1.62]; multiple times per day: β = 0.86 [95% CI, 0.35-1.37]) compared with nonuse, and with greater likelihood of reporting at least moderate depressive symptoms (odds ratio [OR], 1.29 [95% CI, 1.15-1.46]); similar patterns were observed for anxiety and irritability. The highest estimates were observed among individuals using AI for personal use (β = 0.31 [95% CI, 0.10-0.52]) and those aged 25 to 44 years (β = 1.22 [95% CI, 0.70-1.74]) or 45 to 64 years (β = 1.38 [95% CI, 0.72-2.05]). Conclusions and Relevance: This survey study found that AI use was significantly associated with greater depressive symptoms, with magnitude of differences varying by age group. Further work is needed to understand whether these associations are causal and explain heterogeneous effects.

Indexed as

DepressionGenerative Artificial IntelligenceAdultAgedFemaleHumansMaleMiddle AgedSocial MediaSurveys and QuestionnairesUnited StatesYoung Adult

Identifiers

PMID41563755
PMCPMC12824790

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.