The Applications of Large Language Models in Mental Health: Scoping Review

BACKGROUND: Mental health is emerging as an increasingly prevalent public issue globally. There is an urgent need in mental health for efficient detection methods, effective treatments, affordable privacy-focused health care solutions, and increased access to specialized psychiatrists. The emergence and rapid development of large language models (LLMs) have shown the potential to address these mental health demands. However, a comprehensive review summarizing the application areas, processes, and performance comparisons of LLMs in mental health has been lacking until now. OBJECTIVE: This review aimed to summarize the applications of LLMs in mental health, including trends, application areas, performance comparisons, challenges, and prospective future directions. METHODS: A scoping review was conducted to map the landscape of LLMs' applications in mental health, including trends, application areas, comparative performance, and future trajectories. We searched 7 electronic databases, including Web of Science, PubMed, Cochrane Library, IEEE Xplore, Weipu, CNKI, and Wanfang, from January 1, 2019, to August 31, 2024. Studies eligible for inclusion were peer-reviewed articles focused on LLMs' applications in mental health. Studies were excluded if they (1) were not peer-reviewed or did not focus on mental health or mental disorders or (2) did not use LLMs; studies that used only natural language processing or long short-term memory models were also excluded. Relevant information on application details and performance metrics was extracted during the data charting of eligible articles. RESULTS: A total of 95 articles were drawn from 4859 studies using LLMs for mental health tasks. The applications were categorized into 3 key areas: screening or detection of mental disorders (67/95, 71%), supporting clinical treatments and interventions (31/95, 33%), and assisting in mental health counseling and education (11/95, 12%). Most studies used LLMs for depression detection and classification (33/95, 35%), clinical treatment support and intervention (14/95, 15%), and suicide risk prediction (12/95, 13%). Compared with nontransformer models and humans, LLMs demonstrate higher capabilities in information acquisition and analysis and efficiently generating natural language responses. Various series of LLMs also have different advantages and disadvantages in addressing mental health tasks. CONCLUSIONS: This scoping review synthesizes the applications, processes, performance, and challenges of LLMs in the mental health field. These findings highlight the substantial potential of LLMs to augment mental health research, diagnostics, and intervention strategies, underscoring the imperative for ongoing development and ethical deliberation in clinical settings.