ArticleInternational dental journal2025
Performance of Large Language Models on the Korean Dental Licensing Examination: A Comparative Study.
Article in International dental journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of deep machine learning in dental education: a systematic review of effectiveness in dental students' teaching learning outcomes.Evidence-based dentistry · 2026Pooled it
- DentalEdu-AI graph: mapping evidence-grounded and knowledge-structured AI for dental education, assessment, and accuracy.The Saudi dental journal · 2026Article
- Benchmarking GPT-5, Gemini 2.5 Pro, Grok 4, and other LLMs on pediatric dentistry questions from a dental specialization exam.Scientific reports · 2026Article
- Comparative Performance of State-of-the-Art LLMs on the KDLE: A 2025 Benchmark Study.International dental journal · 2026Article
- Bi-linguistic performance of large language models in multimodal analysis for differentiating jawbone-destroying malignancy from osteomyelitis.BMC oral health · 2026Article
- Evaluating open-source LLMs for dental EMR generation.BMC oral health · 2026Article
- A Survey on Medical Competence Evaluation Benchmarks for Large Language Models.Health care science · 2026Review
- Performance of GPT-5, DeepSeek, and Claude in dental MCQs for medically compromised patients.Journal of translational medicine · 2026Article
- Analysis of multimodal large language models on visually-based questions in the Japanese National Examination for Dental Hygienists: A preliminary comparative study.Journal of dental sciences · 2026Article
- A Scoping Review of Large Language Models in Dental Education: Applications, Challenges, and Prospects.International dental journal · 2025Article
- Analyzing the performance of multimodal large language models on visually-based questions in the Japanese National Examination for Dental Technicians.Journal of dental sciences · 2025Article
- Benchmarking multimodal large language models on the dental licensing examination: Challenges with clinical image interpretation.Journal of dental sciences · 2025Article
- Application of large language models in oral health education: a review of the literature.Canadian journal of dental hygiene : CJDH = Journal canadien de l'hygiene dentaire : JCHD · 2025Review
- Artificial intelligence performance in answering multiple-choice oral pathology questions: a comparative analysis.BMC oral health · 2025Article
- Enhancing patient-centered information on implant dentistry through prompt engineering: a comparison of four large language models.Frontiers in oral health · 2025Article
- How valuable are the questions and answers generated by large language models in oral and maxillofacial surgery?PloS one · 2025Article
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Authors and funding
3 authors.
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Abstract
purposeThis study investigated the potential application of large language models (LLMs) in dental education and practice, with a focus on ChatGPT and Claude3-Opus. Using the Korean Dental Licensing Examination (KDLE) as a benchmark, we aimed to assess the capabilities of these models in the dental field.
methodsThis study evaluated three LLMs: GPT-3.5, GPT-4 (version: March 2024), and Claude3-Opus (version: March 2024). We used the KDLE questionnaire from 2019 to 2023 as inputs to the LLMs and then used the outputs from the LLMs as the corresponding answers. The total scores for individual subjects were obtained and compared. We also compared the performance of LLMs with those of individuals who underwent the exams.
resultsClaude3-Opus performed best among the considered LLMs, except in 2019 when ChatGPT-4 performed best. Claude3-Opus and ChatGPT-4 surpassed the cut-off scores in all the years considered; this indicated that Claude3-Opus and ChatGPT-4 passed the KDLE, whereas ChatGPT-3.5 did not. However, all LLMs considered performed worse than humans, represented here by dental students in Korea. On average, the best-performing LLM annually achieved 85.4% of human performance.
conclusionUsing the KDLE as a benchmark, our study demonstrates that although LLMs have not yet reached human-level performance in overall scores, both Claude3-Opus and ChatGPT-4 exceed the cut-off scores and perform exceptionally well in specific subjects. CLINICAL RELEVANCE: Our findings will aid in evaluating the feasibility of integrating LLMs into dentistry to improve the quality and availability of dental services by offering patient information that meets the basic competency standards of a dentist.
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