A data-driven method for research trend analysis in a scientific discipline: Application to the journal of biomedical informatics
This study presents a method that leverages modern generative AI technologies for retrospective analysis of a scientific field to identify emerging topics and to detect shifts in scholarly focus. Illustrated by data for JBI and correlated with historical background events and policy changes, our findings demonstrate the effectiveness and utility of the methods while providing a powerful lens to understand the evolution of biomedical informatics research priorities in JBI.
Authors
- Gongbo Zhang (ORCID: https://orcid.org/0009-0001-0077-3615)
- Mor Peleg (ORCID: https://orcid.org/0000-0002-5612-768X)
- Edward H. Shortliffe (ORCID: https://orcid.org/0000-0001-5201-6176)
- Yilu Fang (ORCID: https://orcid.org/0000-0002-2681-1931)
- Chunhua Weng
- Fangyi Chen
- Vimla L. Patel
- Samir Sanchez Tejada
Institutions
- New York Academy of Sciences (US)
- Columbia University Irving Medical Center (US)
- Columbia University (US)
- University of Haifa (IL)
Publication Details
- Journal
- Journal of Biomedical Informatics
- Published
- 2026-03-14
- DOI
- https://doi.org/10.1016/j.jbi.2026.105013
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00