收藏切换
Applications of large language models in mental health interventions: feasibility, current status, and future trends
收藏切换
Xu XUE1, Kun CHEN1, Jia-wen LIU1, Peng JIA2, 3, Bin YU1, 4, Shu-juan YANG1, 3, 5
Modern Preventive Medicine | 2025, 52(22) : 4194 - 4200
Less
收藏切换
Modern Preventive Medicine | 2025, 52(22): 4194-4200
• Clinical Medicine and Prevention •
Applications of large language models in mental health interventions: feasibility, current status, and future trends
Full
Xu XUE1, Kun CHEN1, Jia-wen LIU1, Peng JIA2, 3, Bin YU1, 4, Shu-juan YANG1, 3, 5
Affiliations
  • West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
Published: 2025-11-25 doi: 10.20043/j.cnki.MPM.202505303
Outline
收藏切换
Objective

To systematically review the current research status, feasibility, and future prospects of using large language models(LLMs)for mental health interventions.

Methods

We searched literature published between January 2023 and March 2025 on LLM-based mental health interventions across databases including CNKI, Wanfang Data, VIP, PubMed, Scopus, and Web of Science. After screening, the Mixed Methods Appraisal Tool(MMAT)was used for quality assessment. Key data were extracted, including study characteristics, model types, and supported languages.

Results

Twenty-one English-language studies were included. Twelve studies employed population-based intervention trials, investigating the effects of LLMs in reducing anxiety, identifying and intervening in cognitive distortions, measuring and fostering psychological resilience in children, alleviating depression and loneliness, and enhancing willingness to engage in mindfulness training. Results indicated that LLMs demonstrated some efficacy in these tasks. Another seven studies supported the feasibility of LLM-based mental health interventions in three key areas: psychological knowledge consultation, emotional support, and therapeutic assistance. However, challenges such as bias, cultural adaptability, and privacy concerns remain. Seven studies used English as the model language, while thirteen applied prompt engineering or instruction fine-tuning to adapt the models, with adjusted models showing improved performance in specific mental health tasks. Quality assessment revealed that existing studies were limited.

Conclusion

LLMs show promising potential in mental health promotion. Future research should refine model strategies to enhance task performance and incorporate rigorously designed empirical research to ensure scientifically robust efficacy evaluations.

Mental health promotion  /  Artificial intelligence  /  Large language models
薛旭, 陈坤, 刘佳雯, 贾鹏, 余彬, 杨淑娟. 大语言模型在心理健康干预中的研究进展. 现代预防医学, 2025 , 52 (22) : 4194 -4200 . DOI: 10.20043/j.cnki.MPM.202505303
Xu XUE, Kun CHEN, Jia-wen LIU, Peng JIA, Bin YU, Shu-juan YANG. Applications of large language models in mental health interventions: feasibility, current status, and future trends[J]. Modern Preventive Medicine, 2025 , 52 (22) : 4194 -4200 . DOI: 10.20043/j.cnki.MPM.202505303
Year 2025 volume 52 Issue 22
48
23
Cite this Article
BibTeX
Article Info
doi: 10.20043/j.cnki.MPM.202505303
  • Receive Date:2025-05-22
  • Online Date:2026-10-29
  • Published:2025-11-25
Article Data
Affiliations
History
  • Received:2025-05-22
Funding
Affiliations
    West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
References
Share
https://castjournals.cast.org.cn/joweb/xdyfyx/EN/10.20043/j.cnki.MPM.202505303
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT