集成技术2026，Vol.15Issue(1)：85-107,23.DOI:10.12146/j.issn.2095-3135.20251102001
大语言模型在心理健康领域的应用综述
Review on the Application of Large Language Models in the Mental Health Field
摘要
Abstract
The application of large language models(LLMs)in the mental health field has emerged as a core research direction at the intersection of artificial intelligence(AI)and clinical psychology.This review systematically synthesizes the research progress in this domain from 3 dimensions:model characteristics and empirical evidence,clinical application scenarios,and technological development pathways.At the level of model characteristics and empirical evidence,this paper analyzes the core traits of LLMs and summarizes the empirical support for their applicability in psychological symptom diagnosis and mental illness intervention.In terms of clinical applications,it systematically summarizes the practical cases and application effectiveness of LLMs in scenarios such as mental illness diagnosis,psychological state assessment,virtual psychotherapy,and clinical decision support.From the perspective of technological development,it focuses on sorting out the key advances in directions including data construction,model capability enhancement,and specialized evaluation methods tailored to the mental health field.Finally,the review explicitly points out the core challenges currently facing the research field—such as the disconnect between diagnostic outcomes and clinical practice,insufficient depth of therapeutic simulation,scarcity of high-quality annotated data,and the lack of clinical translation and validation of technologies—and presents an outlook on the future development directions for clinical application implementation and technological innovation research.关键词
大语言模型/心理健康/症状诊断/治疗干预Key words
large language models/mental health/symptom diagnosis/therapeutic intervention分类
信息技术与安全科学引用本文复制引用
金约汗,谭明环,杨敏..大语言模型在心理健康领域的应用综述[J].集成技术,2026,15(1):85-107,23.基金项目
国家自然科学基金项目(62406314) （62406314）
广东省基础与应用基础研究基金项目(2023A1515110496) （2023A1515110496）
中国博士后科学基金项目(2023M733654) This work is supported by National Natural Science Foundation of China(62406314),Guangdong Basic and Applied Basic Research Foundation(2023A1515110496),China Postdoctoral Science Foundation(2023M733654) （2023M733654）
