ArticleJournal of dental sciences2026
Evaluation of GPT-4o and Gemini Advanced on the Korean National Dental Licensing Examination: Accuracy, consistency, and question generation.
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 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Multimodal Large Language Models for Dental Chart Image Interpretation: Cross-Sectional Benchmarking Study With Students and Clinicians.JMIR medical education · 2026Article
- Agentic GPT-5.0 system outperforms standard large language models and human experts in critical care clinical decision-making: a simulation study.BMC medical informatics and decision making · 2026Article
- Generative Artificial Intelligence-driven orthodontic education practices.BMC medical education · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/purpose: Large language models (LLMs), such as GPT-4o and Gemini Advanced, have performed strongly on global medical examinations. However, their capabilities in non-English, dentistry-specific licensing contexts remain unclear. Thus, this study aimed to compare the performance, consistency, and question-generation abilities of GPT-4o and Gemini Advanced in the Korean National Dental Licensing Examination (KNDLE). Materials and methods: This study used 1,401 text-based KNDLE questions from 2019 to 2023 in Korean. Each model responded to the questions in three separate runs. Accuracy and consistency were compared with human answers. The models generated new questions in four subject areas and attempted to solve each other's generated items. Paired t-tests and chi-square tests were conducted. Results: GPT-4o achieved significantly higher average accuracy than Gemini Advanced (81.1 % vs. 76.6 %, Conclusion: GPT-4o outperformed Gemini Advanced in accuracy, consistency, and alignment with its generated content. However, challenges remain in clinical domains and cross-model understanding, highlighting the potential of LLMs as supportive tools for non-English dental education and question generation while emphasizing the persistent need for expert oversight and domain-specific refinement.
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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.