Large Language Models for Mental Health Applications: Systematic Review
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Abstract
Background: Large language models (LLMs) are advanced artificial neural networks trained on extensive datasets to accurately understand and generate natural language. While they have received much attention and demonstrated potential in digital health, their application in mental health, particularly in clinical settings, has generated considerable debate. Objective: This systematic review aims to critically assess the use of LLMs in mental health, specifically focusing on their applicability and efficacy in early screening, digital interventions, and clinical settings. By systematically collating and assessing the evidence from current studies, our work analyzes models, methodologies, data sources, and outcomes, thereby highlighting the potential of LLMs in mental health, the challenges they present, and the prospects for their clinical use. Methods: Adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, this review searched 5 open-access databases: MEDLINE (accessed by PubMed), IEEE Xplore, Scopus, JMIR, and ACM Digital Library. Keywords used were (mental health OR mental illness OR mental disorder OR psychiatry) AND (large language models). This study included articles published between January 1, 2017, and April 30, 2024, and excluded articles published in languages other than English. Results: In total, 40 articles were evaluated, including 15 (38%) articles on mental health conditions and suicidal ideation detection through text analysis, 7 (18%) on the use of LLMs as mental health conversational agents, and 18 (45%) on other applications and evaluations of LLMs in mental health. LLMs show good effectiveness in detecting mental health issues and providing accessible, destigmatized eHealth services. However, assessments also indicate that the current risks associated with clinical use might surpass their benefits. These risks include inconsistencies in generated text; the production of hallucinations; and the absence of a comprehensive, benchmarked ethical framework. Conclusions: This systematic review examines the clinical applications of LLMs in mental health, highlighting their potential and inherent risks. The study identifies several issues: the lack of multilingual datasets annotated by experts, concerns regarding the accuracy and reliability of generated content, challenges in interpretability due to the “black box” nature of LLMs, and ongoing ethical dilemmas. These ethical concerns include the absence of a clear, benchmarked ethical framework; data privacy issues; and the potential for overreliance on LLMs by both physicians and patients, which could compromise traditional medical practices. As a result, LLMs should not be considered substitutes for professional mental health services. However, the rapid development of LLMs underscores their potential as valuable clinical aids, emphasizing the need for continued research and development in this area. Trial Registration: PROSPERO CRD42024508617; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=508617
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Systematic and scoping reviews show that LLM-CB risks (e.g., consistencies, hallucinations, ethical risks) may outweigh potential benefits.
“Systematic and scoping reviews show that LLM-CB risks (e.g., consistencies, hallucinations, ethical risks) may outweigh potential benefits33,76,77.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [76]
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- Authors
- Guo, Z., Lai, A., Thygesen, J. H., Farrington, J., Keen, T., & Li, K.
- Affiliations
- University College London (GB)
- Corresponding author
- Li, Kezhi
- Venue
- JMIR Mental Health
- Publisher
- JMIR Publications Inc.
- Published
- 2024-10-18
- Volume
- 11
- Pages
- e57400
- ISSN
- 2368-7959 (ISSN-L)
- PMC
- PMC11530718
- OpenAlex
- W4402193006
- Cited by
- 269
- References
- 96
- Citations by year
- 2026: 174 · 2025: 90 · 2024: 5
- FWCI
- 23.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Mental Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- UK Research and Innovation (EP/S021612/1)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- large language models, mental health, mental health screening, suicidal ideation detection, chatbots, digital interventions, hallucinations, ethical risks, data privacy, e-health, clinical implementation, black-box interpretability, overreliance on AI, digital mental health services, stigma reduction, mental health literacy
- MeSH terms
- Mental Disorders, Humans, Mental Health, Natural Language Processing, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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