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A data-driven analysis of artificial intelligence applications in depression research: 2020-2025
Da Shao1, Lamei Shao2, Zengwei Kou3
1Research Center of Translational Medicine, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China.
Asian Journal of Psychiatry
|April 17, 2026
Summary
Artificial intelligence (AI) is increasingly used in depression research, with significant publication growth since 2020. Key areas include AI-driven diagnosis, symptom classification, and risk prediction for mental health.
Area of Science:
- Psychiatry and Mental Health
- Computer Science and Artificial Intelligence
- Bibliometrics and Scientometrics
Background:
- The integration of artificial intelligence (AI) into depression research is rapidly expanding.
- Understanding current trends and key players is crucial for advancing AI-assisted mental health solutions.
Purpose of the Study:
- To conduct a bibliometric analysis of AI applications in depression research.
- To identify publication trends, leading institutions and countries, prolific authors, and emerging research themes.
Main Methods:
- Bibliometric analysis of AI in depression research publications.
- Keyword co-occurrence analysis to identify research correlations.
- Trend analysis of publication volume and geographical distribution.
Main Results:
- A substantial growth in publications since 2020, led by China and the United States.
- Prominent contributions from institutions like the University of Toronto and Sichuan University.
- Strong correlations found between "machine learning" and "prediction," and "feature extraction" and "electroencephalography."
Conclusions:
- AI applications in depression research show diverse patterns based on demographics, despite shared methodological frameworks.
- The study provides a comprehensive overview of AI in depression research, highlighting key contributors and future directions.
- Valuable insights are offered for interdisciplinary development in AI-assisted mental health.
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