AI Applications in Depression Detection and Diagnosis: Bibliometric and Visual Analysis of Trends and Future Directions
The integration of AI in depression diagnosis is a rapidly maturing and diversifying field, transitioning from theoretical exploration to clinically relevant applications that emphasize objective, data-driven approaches. The identified trends underscore the need for enhanced interdisciplinary and international collaboration, the development of ethical frameworks, and a focus on translating technological innovations into accessible and equitable mental health solutions. These findings offer valuable insights for researchers, clinicians, and policy makers to strategically advance AI-assisted depression diagnostics globally.
Authors
- Xiali Xue (ORCID: https://orcid.org/0000-0003-0727-6459)
- Wenbo Ren (ORCID: https://orcid.org/0000-0002-2167-590X)
- J. Huang (ORCID: https://orcid.org/0000-0002-1406-8669)
- Lu Liu (ORCID: https://orcid.org/0000-0003-2596-3893)
Institutions
- Peking University (CN)
- Zhejiang Provincial People's Hospital (CN)
- Peking University Third Hospital (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Hangzhou Medical College (CN)
Publication Details
- Journal
- JMIR Mental Health
- Published
- 2025-09-27
- DOI
- https://doi.org/10.2196/79293
- Citations
- 2
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 4.24