Global evolution of AI-related infectious disease research: a bibliometric and LLM-assisted analysis
Artificial intelligence (AI) is reshaping infectious disease research, but research attention alignment with disease burden remains unclear. This study analyzed 9026 publications (1995–2026) using bibliometric, GPT-4o-mini-assisted extraction, and spatial analysis. Results show a US–China dual-center pattern, evolving AI applications, and persistent mismatch between research attention and disease burden. Research capacity and socioeconomic factors, not disease burden, primarily determined distribution. This framework enables global assessment of research priorities.
Authors
- Yujuan Yue (ORCID: https://orcid.org/0000-0002-6949-6820)
- Shaohua Wang (ORCID: https://orcid.org/0000-0001-8651-9505)
- Chunxiang Cao (ORCID: https://orcid.org/0000-0002-4007-1546)
- Min Xu
- Yingling Chen
- Xiaoye Wang
Institutions
- Chinese Center For Disease Control and Prevention (CN)
- Chinese Academy of Sciences (CN)
- Chinese Preventive Medicine Association (CN)
- Aerospace Information Research Institute (CN)
- National Institute for Communicable Disease Control and Prevention (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-09-21
- DOI
- https://doi.org/10.1038/s41746-026-03255-4
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00