Evidence map›Paper›PMID 41785452›Full record

ArticleJournal of medical Internet research2026

Psychosis Risk and Generative Artificial Intelligence Use Frequency, Motivations, and Delusion-Like Experiences: Cross-Sectional Survey Study.

Benjamin Buck, Anne Julia Maheux

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

2 authors.

Benjamin BuckDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID https://orcid.org/0000-0003-2841-0493
Anne Julia MaheuxDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID https://orcid.org/0000-0003-1308-5798

Funding

User-centered design of a mobile health intervention to promote help-seeking and reduce duration of untreated illness among young adults with early psychosisK23MH122504 · NIMH · UNIVERSITY OF WASHINGTON · PI BUCK, BENJAMIN EDWARDS · 2020 to 2024
$930k
NIMH NIH HHS K23 MH122504
6 · The paper itself

Abstract

backgroundGrowth of generative artificial intelligence (GenAI) has exploded in recent years. Many have noted its substantial potential to increase access to scalable digital mental health interventions or provide companions for individuals who are socially isolated. At the same time, seeking mental health support from mainstream GenAI models may involve risks. Several recent examples of exacerbation of delusions have received attention in the popular press, leading to a call for empirical research to document the scope of interactions with GenAI among individuals experiencing symptoms of psychosis.

objectiveThis study aimed to evaluate associations of psychosis risk to GenAI use frequency, motivations for use, and GenAI interactions involving potential delusions.

methodsWe conducted a large-scale cross-sectional survey of 1003 young adults in United States, divided the sample of individuals that had used GenAI into "elevated risk" (Prodromal Questionnaire, Brief Version Distress Score ≥20; N=267, 28%) and "low risk" groups (Prodromal Questionnaire, Brief Version Distress Score <20; N=685, 72%), and compared groups on several assessments related to GenAI use.

resultsWe found that while members of the elevated risk group were no more likely to have ever used GenAI, they were significantly more likely to report intensive use (odds ratio 1.70 to 2.56; ie, several times per day, more than 30 minutes per day, 6 or more chatbot conversations per day). Those at elevated risk were more likely to report using GenAI to receive social and emotional support and significantly more likely to ascribe human-like roles to their chatbot interactions (odds ratio 1.76 to 3.08; ie, companion, friend, therapist, and romantic partner). Delusion-related interactions were also commonly reported among those at risk for psychosis (item endorsements from 13.3% to 30.7%).

conclusionsWhile it is unclear whether they have a positive or negative impact overall, GenAI chatbots may have the potential to impact symptom-related experiences among young adults at risk.

Indexed as

DelusionsGenerative Artificial IntelligenceMotivationPsychotic DisordersAdolescentAdultCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesUnited StatesYoung Adultartificial intelligencedigital healthdigital mental healthgenerative AImental healthpsychosispsychosis riskyoung adults

Identifiers

PMID41785452
PMCPMC13003207

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.