ArticleFrontiers in psychiatry2025
Evaluation of large language models on mental health: from knowledge test to illness diagnosis.
Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of Chatbots in Mental Health Screening and Assessment: Systematic Review.JMIR mental health · 2026Pooled it
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- Artificial Intelligence in Psychiatry Training: Comparative Insights from Nine Large Language Models Across Cultural and Exam Contexts.The Psychiatric quarterly · 2026Article
- Article
- Evaluating the Efficacy of AI-Based Interactive Assessments Using Large Language Models for Depression Screening: Development and Usability Study.JMIR formative research · 2026Article
- Assessing large language model responses to pediatric depression FAQs: a cross-sectional study on readability, accuracy, and sentiment.Frontiers in psychiatry · 2026Article
- Article
- Constructing and applying a multi-turn psychological support dialogue corpus based on the Helping Skills Chain-of-Thought.Frontiers in psychology · 2026Article
- Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos.Algorithms · 2025Article
- Evaluation of large language models in rheumatology and clinical immunology: a systematic assessment based on Chinese national health professional qualification examination.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have opened up new possibilities in the field of mental health, offering applications in areas such as mental health assessment, psychological counseling, and education. This study systematically evaluates 15 state-of-the-art LLMs, including DeepSeekR1/V3 (March 24, 2025), GPT-4.1 (April 15, 2025), Llama4 (April 5, 2025), and QwQ (March 6, 2025, developed by Alibaba), on two key tasks: mental health knowledge testing and mental illness diagnosis in the Chinese context. We use publicly available datasets, including Dreaddit, SDCNL, and questions from the CAS Counsellor Qualification Exam. Results indicate that DeepSeek-R1, QwQ, and GPT-4.1 outperform other models in both knowledge accuracy and diagnostic performance. Our findings highlight the strengths and limitations of current LLMs in Chinese mental health scenarios and provide clear guidance for selecting and improving models in this sensitive domain.
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