Analyzing the performance of multimodal large language models on visually-based questions in the Japanese National Examination for Dental Technicians
These results suggest that multimodal LLMs can supplement theoretical dental technology education, although their limited performance on visual tasks indicates the need for traditional hands-on training. Enhanced image interpretation skills may help address workforce challenges in dental technology.
Authors
- Hiroki Nikawa (ORCID: https://orcid.org/0000-0002-4883-5723)
- Naoya Kakimoto (ORCID: https://orcid.org/0000-0003-1274-8991)
- Masato Kaku (ORCID: https://orcid.org/0000-0002-3878-749X)
- Tzu‐Yu Peng (ORCID: https://orcid.org/0000-0002-7168-9429)
- Saiji Shimoe (ORCID: https://orcid.org/0000-0003-2274-8354)
- Tsuyoshi Taji (ORCID: https://orcid.org/0000-0002-1286-5357)
- Yuichi Mine (ORCID: https://orcid.org/0000-0002-7057-1955)
- Shota Okazaki
- Saori Takeda
- Takeshi MURAYAMA
Institutions
- Hiroshima University (JP)
- Taipei Medical University (TW)
Publication Details
- Journal
- Journal of Dental Sciences
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1016/j.jds.2025.02.022
- Citations
- 5
- Primary Topic
- Dental Research and COVID-19
- Type
- article
- Field-Weighted Citation Impact
- 14.12