Emulated trial of artificial intelligence use and subsequent depressive outcomes in a survey of US adults

Clinical Snapshot

Key Findings

• P Value: p=0.65

• Effect Size: Mean difference -0.18 points

• Primary Outcome: Change in PHQ-9 depressive symptom score

• Nnt Or Sensitivity: Effect size below minimal clinically important difference for PHQ-9 (typically 2-3 points)

• Confidence Interval: 95% CI -0.94 to 0.59

Clinical Application

Results suggest routine AI use monitoring for depression risk may not be warranted in general population Limited direct applicability given US-specific survey; Australian AI adoption patterns and mental health service frameworks differ. RACGP guidelines on digital mental health may need consideration of AI-specific risks in vulnerable populations General adult population using AI technologies in work, school, or personal contexts

Abstract

BACKGROUND: Generative artificial intelligence (AI) use has been suggested to have adverse mental health consequences but a causal relationship has not been examined. OBJECTIVE: To simulate a randomised controlled trial of AI use in a work, school or personal context by applying target trial emulation to multiple waves of data from a nationally representative survey. METHODS: We conducted a target trial emulation using non-probability survey data from three waves of a nationally representative survey conducted between 18 June 2024 and 8 January 2025. Participants aged ≥18 years reported generative AI use frequency at baseline. High-frequency use was defined as multiple times per week or more. The primary outcome was depressive symptom severity measured using the Patient Health Questionnaire 9-item (PHQ-9) at follow-up. Generalised causal forests assessed heterogeneity of treatment effects. FINDINGS: Among 19 099 participants assessed at baseline, 2862 (15.0%) reported AI use at least multiple times per week. A subset of 3109 (16.3%) returned for follow-up. In the primary weighted analysis, high-frequency use was not significantly associated with change in PHQ-9 score at follow-up (mean difference -0.18, 95% CI -0.94 to 0.59; p=0.65). Multiple sensitivity analyses using alternate outcome definitions also did not identify significant causal effects. Generalised causal forests yielded no significant evidence of heterogeneity of effect (p=0.81). CONCLUSIONS: In an emulated randomised trial among US adults, generative AI use was not associated with subsequent depressive symptoms. This result does not support the premise that AI use causes greater depressive symptoms, although adverse outcomes among vulnerable individuals cannot be excluded. CLINICAL IMPLICATIONS: AI use is unlikely to cause increased depressive symptoms among most US adults. Continued monitoring should clarify potential risks among vulnerable populations.