Benchmarking multimodal large language models on the dental licensing examination: Challenges with clinical image interpretation
Multimodal LLMs demonstrate promising performance on dental examination questions, particularly in text-based scenarios, but significant challenges remain in complex visual interpretation. The remarkable zero-shot performance of newer models such as o1 suggests potential applications in dental education and certain aspects of clinical decision support, although further advances are needed before reliable application in visually complex diagnostic workflows.
Authors
- Naoya Kakimoto (ORCID: https://orcid.org/0000-0003-1274-8991)
- Tsuyoshi Taji (ORCID: https://orcid.org/0000-0002-1286-5357)
- Yuichi Mine (ORCID: https://orcid.org/0000-0002-7057-1955)
- Hiroyuki Kawaguchi (ORCID: https://orcid.org/0000-0001-8775-1082)
- Shota Okazaki
- Takeshi MURAYAMA
Institutions
- Hiroshima University (JP)
- Hiroshima University Hospital (JP)
Publication Details
- Journal
- Journal of Dental Sciences
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1016/j.jds.2025.03.018
- Citations
- 11
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 7.20