Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis - Janni Leung; Benjamin Johnson; Kelsey McRae; Stephanie Fong; Paula Cardona Gonzalez; Caitlin McClure-Thomas; Tianze Sun; Naomi Kern; Yuen Ming Wong; Richard Liu; Gary Chung Kai Chan (JMIR AI, 2026)
Abstract Background General-purpose large language models (LLMs) are increasingly being tested in mental health care, where language is central to assessment, diagnosis, risk evaluation, therapeutic interaction, monitoring, and patient education. However, their clinical usefulness, safety, and readiness for implementation remain uncertain. Existing reviews have largely been descriptive or scoping in nature, and broad health care reviews have not examined in detail the distinctive risks and applications of LLMs in mental health care. Objective We aim to systematically review empirical evidence on the use of general-purpose LLMs in mental health care; characterize the clinical tasks, study designs, models, outcomes, and methodological quality of the evidence; and synthesize findings across clinically meaningful task domains, including quantitative synthesis where sufficiently comparable studies were available. Methods We followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and searched PubMed, Embase, ACM Digital Library, IEEE Xplore, and Google Scholar from November 2022 to March 2026. Eligible studies reported quantitative data evaluating general-purpose LLMs for direct mental health care tasks. Methodological quality was assessed using the Mixed Methods Appraisal Tool and certainty of evidence using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) domains. Findings were narratively synthesized, and random-effects meta-analyses were conducted on studies that tested LLMs on screening and diagnosis by ChatGPT-4 (OpenAI), ChatGPT-3.5 (OpenAI), and GPT-3 (OpenAI) models for mental health outcomes that reported on specificity and sensitivity. Hartung-Knapp adjustments were applied, and prediction intervals (PIs) estimated. Results We included 66 studies, comprising 37 vignette or simulation studies, 22 retrospective studies, and 7 prospective studies. Applications included screening and diagnosis (n=29), clinical decision support (n=14), treatment support (n=10), documentation and monitoring (n=6), patient education (n=4), and patient engagement (n=3). In screening and diagnosis, meta-analysis of 8 studies found that GPT-4 had the higher pooled sensitivity (0.83, 95% CI 0.38‐0.97; 95% PI 0.02‐1.00) than GPT-3.5 (0.70, 95% CI 0.13‐0.97; 95% PI 0.00‐1.00) and GPT-3 (0.61, 95% CI 0.33‐0.82; 95% PI 0.10‐0.96). However, GPT-4 specificity was lower at 0.77 (95% CI 0.52‐0.91; 95% PI 0.10‐0.99). Narrative synthesis suggested that LLMs performed most consistently in structured and linguistically explicit tasks. Certainty of evidence was generally low across domains, although documentation and monitoring reached moderate certainty. Major limitations included indirectness from vignette-based designs, uncertain representativeness, inconsistent outcome reporting, and sparse prospective real-world evaluation. Conclusions General-purpose LLMs show promise for selected mental health care applications. However, current evidence remains too heterogeneous, indirect, and uncertain to support routine unsupervised use, particularly for diagnosis, risk assessment, crisis response, or therapeutic interaction. Broad accessibility should not be mistaken for clinical readiness. Future studies should move beyond simulations and retrospective evaluations toward prospective, real-world research assessing safety, reliability, equity, acceptability, clinical outcomes, and implementation in diverse mental health care settings.