Cross-institutional dental electronic health record entity extraction via generative artificial intelligence and synthetic notes
The study highlights the potential transformative impact of AI and NLP on health-care research. Most clinical documentation (40%-80%) is free text. Scaling our method could enhance clinical data reuse.
Authors
- Muhammad F. Walji (ORCID: https://orcid.org/0000-0001-5306-9683)
- Oluwabunmi Tokede (ORCID: https://orcid.org/0000-0003-4770-4200)
- Guo‐Hao Lin (ORCID: https://orcid.org/0000-0003-1290-9994)
- Xiaoqian Jiang (ORCID: https://orcid.org/0000-0001-9933-2205)
- Yao-Shun Chuang (ORCID: https://orcid.org/0000-0001-9582-9222)
- Chun‐Teh Lee (ORCID: https://orcid.org/0000-0001-7812-5637)
- Ryan Brandon (ORCID: https://orcid.org/0000-0003-1335-2438)
Institutions
- Willamette University (US)
- University of California, San Francisco (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf061
- Citations
- 6
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.93
Funders
- U.S. Department of Health and Human Services
- National Institute of Dental and Craniofacial Research