Development and evaluation of a retrieval-augmented large language model framework for enhancing endodontic education
This proposed framework improves educational outcomes through precise and context-aware knowledge delivery. Furthermore, it represents a scalable and transferable model for AI-enhanced clinical training across medical specialties, significantly advancing competency-based pedagogy in dental education.
Authors
- Xudong Lü (ORCID: https://orcid.org/0000-0001-7658-5250)
- Yanhua Long (ORCID: https://orcid.org/0000-0003-0924-408X)
- Xiaowei Xu (ORCID: https://orcid.org/0000-0003-2863-1376)
- Jiao Li (ORCID: https://orcid.org/0000-0001-6391-8343)
- Zeng Yin
- Lin Zhu (ORCID: https://orcid.org/0000-0001-9530-8531)
- Yanmei Dong (ORCID: https://orcid.org/0000-0002-9154-8949)
- Siyi Liu (ORCID: https://orcid.org/0009-0001-4263-535X)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- Stomatology Hospital (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2025-06-03
- DOI
- https://doi.org/10.1016/j.ijmedinf.2025.106006
- Citations
- 6
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.93
Funders
- Peking University
- Chinese Academy of Medical Sciences Initiative for Innovative Medicine