Jin Yu, Liu Jiayi, Li Pan, Wang Baosen, Yan Yangxinyu, Zhang Huilin, Ni Chenhao, Wang Jing, Li Yi, Bu Yajun, Wang Yuanyuan
Department of Statistics, Faculty of Arts and Sciences, Beijing Normal University, Beijing, China.
School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, Guangdong, China.
J Med Internet Res. 2025 May 5;27:e69284. doi: 10.2196/69284.
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.
This review aimed to summarize the applications of LLMs in mental health, including trends, application areas, performance comparisons, challenges, and prospective future directions.
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 Studies were excluded if they (1) were not peer-reviewed or did not focus on mental health or mental disorders or (2) did not use LLMs; studies that used only natural language processing or long short-term memory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.
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.
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.
心理健康正日益成为全球普遍存在的公共问题。心理健康领域迫切需要高效的检测方法、有效的治疗手段、经济实惠且注重隐私的医疗保健解决方案，以及增加获得专业精神科医生服务的机会。大语言模型（LLMs）的出现和快速发展显示出满足这些心理健康需求的潜力。然而，迄今为止，缺乏一篇全面综述来总结大语言模型在心理健康领域的应用领域、流程和性能比较。
本综述旨在总结大语言模型在心理健康领域的应用，包括趋势、应用领域、性能比较、挑战以及未来的发展方向。
进行了一项范围综述，以梳理大语言模型在心理健康领域的应用情况，包括趋势、应用领域、比较性能和未来发展轨迹。我们检索了7个电子数据库，包括科学网、PubMed、考克兰图书馆、IEEE Xplore、维普、中国知网和万方，检索时间范围为2019年1月1日至2024年8月31日。纳入的研究为聚焦于大语言模型在心理健康领域应用的同行评议文章。如果研究（1）未经过同行评议或未聚焦于心理健康或精神障碍，或者（2）未使用大语言模型，则将其排除；仅使用自然语言处理或长短期记忆模型的研究也被排除。在对符合条件的文章进行数据制表过程中，提取了有关应用细节和性能指标的相关信息。
在4859项使用大语言模型进行心理健康任务的研究中，共筛选出95篇文章。这些应用被分为3个关键领域：精神障碍的筛查或检测（67/95，71%）、支持临床治疗和干预（31/95，33%）以及协助心理健康咨询和教育（11/95，12%）。大多数研究将大语言模型用于抑郁症检测和分类（33/95，35%）、临床治疗支持和干预（14/95，15%）以及自杀风险预测（12/95，13%）。与非Transformer模型和人类相比，大语言模型在信息获取和分析以及高效生成自然语言回复方面表现出更高的能力。不同系列的大语言模型在处理心理健康任务时也各有优缺点。
本范围综述综合了大语言模型在心理健康领域的应用、流程、性能和挑战。这些发现凸显了大语言模型在增强心理健康研究、诊断和干预策略方面的巨大潜力，强调了在临床环境中持续发展和进行伦理考量的必要性。
