Evidence map›Paper›PMID 42798673›Full record

ReviewCurrent pediatrics reports2026

Generative Artificial Intelligence Use and Adolescent Mental Health.

Jason M Nagata, Jacqueline O Hur, Thang Diep, Hasan Alsamman, Eliot D Lee, Alexander Heuer, Henry Huynh, Sahana Nayak, Ryan McBain, Jonathan Cantor and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current pediatrics reports, 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

11 authors.

Jason M NagataDepartment of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0503, San Francisco, CA 94143 USA.ORCID 0000-0002-6541-0604
Jacqueline O HurDepartment of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0503, San Francisco, CA 94143 USA.
Thang DiepDepartment of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0503, San Francisco, CA 94143 USA.
Hasan AlsammanDepartment of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0503, San Francisco, CA 94143 USA.
Eliot D LeeDepartment of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0503, San Francisco, CA 94143 USA.
Alexander HeuerDepartment of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0503, San Francisco, CA 94143 USA.
Henry HuynhDepartment of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0503, San Francisco, CA 94143 USA.
Sahana NayakDepartment of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0503, San Francisco, CA 94143 USA.
Ryan McBainRAND, Arlington, VA USA.
Jonathan CantorRAND, Santa Monica, CA USA.
Jason M LavenderMilitary Cardiovascular Outcomes Research Program (MiCOR), Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD USA.

Funding

Informing national guidelines on adolescent and young adult physical activity and sedentary behavior to prevent cardiovascular diseaseK08HL159350 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NAGATA, JASON M · 2021 to 2025
$869k
NHLBI NIH HHS K08 HL159350
6 · The paper itself

Abstract

Purpose of Review: To synthesize recent literature on the relationships between generative artificial intelligence (GenAI) use and adolescents' mental health, with particular attention to problematic use and psychiatric vulnerabilities. Recent Findings: Emerging evidence suggests associations between GenAI use and a range of adolescent mental health outcomes, including depression, anxiety, psychosis-related experiences, externalizing behaviors, eating disorder risk, and suicidality. Problematic patterns of use, including dependency and emotional reliance, may be particularly relevant among adolescents with preexisting psychiatric vulnerabilities. Current evidence suggests that these relationships may be bidirectional: psychological distress can motivate GenAI use for companionship, emotional support, or escape, while problematic use may reinforce maladaptive beliefs, displace real-world relationships, and amplify existing vulnerabilities. However, evidence remains predominantly cross-sectional, limiting causal inference. Summary: GenAI may interact with and amplify underlying psychiatric vulnerabilities during adolescence, highlighting the importance of understanding patterns and motivations of use. Pediatricians and other clinicians caring for adolescents could consider not only the frequency of GenAI use but also motivations for use, problematic dependency, and socioemotional reliance. Identifying high-risk usage patterns of GenAI through longitudinal research, particularly among youth with preexisting psychiatric risks, may inform the development of tailored screening, intervention, and prevention strategies.

Indexed as

AdolescentAIGenerative Artificial IntelligenceMental healthProblematic AI usePsychopathology

Identifiers

PMID42798673
PMCPMC13612636

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.