Currently submitted to: Journal of Medical Internet Research
Date Submitted: Nov 26, 2024
Open Peer Review Period: Nov 27, 2024 - Jan 22, 2025
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The Applications of Large Language Models in Mental Health: A Scoping Review
ABSTRACT
Background:
Mental health is emerging as an increasingly prevalent public issue, particularly within low- and middle-income nations. There is an urgent need in mental health for efficient detection methods, effective treatments, affordable privacy-focused healthcare solutions, and increased access to specialized psychiatrists. The emergence and rapid development of large language models (LLMs) have been leveraged to address these mental health demands. However, a comprehensive review summarizing the applications, processes, and performance of LLMs in mental health has been lacking up until now.
Objective:
To summarize the applications of LLMs in mental health, encompassing trends, research areas, performance comparisons, and prospective future directions.
Methods:
A scoping review was conducted to map the landscape of LLM applications in mental health, including trends, application domains, comparative performances, and future trajectories. We conducted a search across seven electronic databases, including Web of Science, PubMed, Cochrane Library, IEEE Xplore, Weipu, CNKI, and WanFang, from January 1, 2019, to August 31, 2024. Subsequent data-charting of eligible articles extracted relevant information on application aspects and performance metrics.
Results:
A total of 95 articles were included, drawn from 4,544 studies, employing LLMs for mental health tasks. The applications were categorized into three key areas: screening and detection of mental disorders (n = 67), support for clinical treatments and interventions (n = 31), and assisting in mental health counseling and education (n = 11). The majority of studies utilized LLMs for depression detection and classification (34·7%), clinical treatment support and intervention (14·7%), and suicide risk prediction (12·6%). In comparison with non-transformer models and human experts, LLMs demonstrate superior capabilities in information acquisition and analysis, generating natural-language responses, and addressing complex reasoning problems. Assessments of LLM performances indicate that the majority of LLMs exhibit efficiency and promise in addressing mental health tasks.
Conclusions:
This scoping review synthesizes the applications, processes, and performances of LLMs within 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.
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