ArticleJournal of dental sciences2026
Analysis of multimodal large language models on visually-based questions in the Japanese National Examination for Dental Hygienists: A preliminary comparative study.
Article in Journal of dental sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Impact of language and question types on ChatGPT-4o's performance in answering oral pathology questions from Taiwan National Dental Licensing Examinations.Journal of dental sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/purpose: The importance of oral health is globally recognized, which has increased the demand for qualified dental hygienists. This study assessed the performance of multimodal large language models (LLMs), on the Japanese National Examination for Dental Hygienists, focusing on their ability to answer visually-based questions and evaluating image-recognition capabilities. Materials and methods: The 34th Japanese National Examination for Dental Hygienists (March 2025) supplied 213 multiple-choice questions (74 text-only, 139 visually-based). Five multimodal LLMs were tested: OpenAI o3-mini-high (o3-mh), ChatGPT-4.5 Preview (GPT-4.5), Gemini 2.0 Flash Thinking Experimental (Gemini 2.0), Gemini 2.5 Pro Experimental (Gemini 2.5), and Claude 3.7 Sonnet (Claude 3.7). Performance was evaluated by comparing LLM answers to official correct answers. Cochran's Q test and McNemar's tests with Bonferroni correction were used for statistical analysis. Results: Gemini 2.5 achieved the highest overall correct response rate (85.0 %), followed by Claude 3.7 (77.5 %), o3-mh (77.0 %), GPT-4.5 (76.1 %), and Gemini 2.0 (75.1 %). For text-only questions, Claude 3.7 (91.9 %) performed best. On visually-based questions, Gemini 2.5 was superior (82.0 %), while other models scored around 70-73 %. Gemini 2.5 significantly outperformed, GPT-4.5 and Gemini 2.0 overall, and GPT-4.5 and Claude 3.7 on visually-based questions. Conclusion: Multimodal LLMs, particularly Gemini 2.5, demonstrate significant proficiency on the Japanese National Examination for Dental Hygienists, including questions with visual elements. These findings suggest a growing potential for LLMs as educational tools in dental hygiene. However, current limitations in accuracy and reliability necessitate further refinement and cautious integration into educational and clinical settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.