ArticleJournal of dental sciences2025
Performance of artificial intelligence chatbots in National dental licensing examination.
Article in Journal of dental sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Comparative Evaluation of Large Language Models in Responding to Common Misconceptions Regarding Periodontal Disease and Oral Hygiene.Diagnostics (Basel, Switzerland) · 2026Article
- Accuracy of large language models in the Turkish dental specialization examination (DUS): a multidimensional evaluation across disciplines and question formats.BMC oral health · 2026Article
- Performance and Hallucination Analysis of Large Language Models on European Anesthesiology Examinations: Cross-Sectional Comparative Study.JMIR formative research · 2026Article
- Article
- Evaluation of ChatGPT-4o in oral and maxillofacial surgery examinations: a comparative study of performance on U.S. dental decks and chinese dental licensing examination practice questions.BMC oral health · 2026Article
- Comparative analysis of the performance of artificial intelligence language models on Turkish dental specialty examination questions.BMC oral health · 2026Article
- Comparative performance evaluation of ChatGPT-4 Omni and Gemini Advanced in the Turkish Dentistry Specialization Exam.BMC medical education · 2026Article
- Can artificial intelligence chatbots think like dentists? A comparative analysis based on dental specialty examination questions in restorative dentistry.BMC oral health · 2026Article
- Article
- Performance of ChatGPT-4, Gemini, and DeepSeek-V3 in answering the prompt-engineered questions from the Taiwan National Dental Technician Licensing Examination over a three-week period.Journal of dental sciences · 2026Article
- Performance of ChatGPT-4, Gemini, and DeepSeek-V3 on English-translated questions from the Taiwan national dental Technician Licensing examination over a three-week period.Journal of dental sciences · 2026Article
- Large language models as data-driven engines for benchmarking preventive and clinical knowledge in Chinese dental examinations.Frontiers in oral health · 2026Article
- Performance of ChatGPT-4, Gemini, and DeepSeek-V3 on answering the multiple choice questions from Taiwan national dental technician licensing examinations and their self-learning abilities over a three-week period.Journal of dental sciences · 2025Article
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5 authors.
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Abstract
Background/purpose: The Taiwan dental board exams comprehensively assess dental candidates across twenty distinct subjects, spanning foundational knowledge to clinical fields, using multiple-choice single-answer exams with a minimum passing score of 60 %. This study assesses the performance of artificial intelligence (AI)-powered chatbots (specifically ChatGPT3.5, Gemini, and Claude2), categorized as Large Language Models (LLMs), on these exams from 2021 to 2023. Materials and methods: A total of 2699 multiple-choice questions spanning eight subjects in basic dentistry and twelve in clinical dentistry were analyzed. Questions involving images and tables were excluded. Statistical analyses were conducted using McNemar's test. Furthermore, annual results of LLMs were compared with the qualification rates of human candidates to provide additional context. Results: Claude2 demonstrated the highest overall accuracy (54.89 %) on the Taiwan national dental licensing examinations, outperforming ChatGPT3.5 (49.33 %) and Gemini (44.63 %), with statistically significant differences in performance across models. In the basic dentistry domain, Claude2 scored 59.73 %, followed by ChatGPT3.5 (54.87 %) and Gemini (47.35 %). Notably, Claude2 excelled in biochemistry (73.81 %) and oral microbiology (88.89 %), while ChatGPT3.5 also performed strongly in oral microbiology (80.56 %). In the clinical dentistry domain, Claude2 led with a score of 52.45 %, surpassing ChatGPT3.5 (46.54 %) and Gemini (43.26 %), and showed strong results in dental public health (65.81 %). Despite these achievements, none of the LLMs attained passing scores overall. Conclusion: None of the models achieved passing scores, highlighting their strengths in foundational knowledge but limitations in clinical reasoning.
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