Enhanced large language models for effective screening of depression and anxiety
This work highlights the importance of scalable data-generative pipelines for developing effective mental health LLM tools.
Authors
- Tatia M.C. Lee (ORCID: https://orcid.org/0000-0002-9992-6609)
- Minlie Huang (ORCID: https://orcid.org/0000-0001-7111-1849)
- Mengxia Gao (ORCID: https://orcid.org/0000-0003-3393-5497)
- Sahand Sabour
- Zhuang Chen (ORCID: https://orcid.org/0000-0002-7048-7833)
- June M. Liu (ORCID: https://orcid.org/0000-0001-9605-3606)
Institutions
- Center for Information Technology (US)
- Beijing Academy of Artificial Intelligence (CN)
- Innopage (China) (CN)
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
- University of Hong Kong - Shenzhen Hospital (CN)
- Intelligent Health (United Kingdom) (GB)
- University of Hong Kong (HK)
- Tsinghua University (CN)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1038/s43856-025-01158-1
- Citations
- 1
- Primary Topic
- Mental Health via Writing
- Type
- article
- Field-Weighted Citation Impact
- 2.44
Funders
- University of Hong Kong