ArticleJournal of dental sciences2024
Evaluating the efficacy of leading large language models in the Japanese national dental hygienist examination: A comparative analysis of ChatGPT, Bard, and Bing Chat.
Article in Journal of dental sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis.International dental journal · 2026Pooled it
- Large Language Models in Dental Licensing Examinations: Systematic Review and Meta-Analysis.International dental journal · 2025Pooled it
- Accuracy of large language models in the Turkish dental specialization examination (DUS): a multidimensional evaluation across disciplines and question formats.BMC oral health · 2026Article
- Evaluation of the accuracy, quality, and readability of large language models on local anesthesia and general anesthesia-sedation in dentistry.BMC anesthesiology · 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
- Artificial intelligence-powered chatbots' responses to orthodontic questions from the dentistry specialization examination: Accuracy and source evaluation.Journal of dental sciences · 2026Article
- 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
- Evaluation of large language models in medical examinations: A scoping review protocol.PloS one · 2026Article
- Applications and potential of ChatGPT in dentistry: Scoping review of research perspectives.Journal of dental sciences · 2026Review
- Evaluation of GPT-4o and Gemini Advanced on the Korean National Dental Licensing Examination: Accuracy, consistency, and question generation.Journal of dental sciences · 2026Article
- Examiner stratification reveals clinically relevant variability in large language model answers to endodontic patient questions.Frontiers in medicine · 2026Article
- Comparative performance of ChatGPT-4o, ChatGPT-5, and gemini 2.5 flash on Persian internal medicine subspecialty board exams.Scientific reports · 2025Article
- Comparison of chatbots' accuracy in endodontics questions in dentistry specialization exam in Türkiye: ChatGPT-4o, Gemini Advanced, Copilot, and Claude.BMC oral health · 2025Article
- Limitations of knowledge competency and error patterns in large language models based on orthodontic licensing examinations.BMC oral health · 2025Article
- Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network.BMC oral health · 2025Article
- Comparison of accuracy and consistency of AI Language models when answering standardised dental MCQs.BMC medical education · 2025Article
- Diagnostic accuracy of generative large language artificial intelligence models for the assessment of dental crowding.BMC oral health · 2025Article
- 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
- 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
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/purpose: Large language models (LLMs) such as OpenAI's ChatGPT, Google's Bard, and Microsoft's Bing Chat have shown potential as educational tools in the medical and dental fields. This study evaluated their effectiveness using questions from the Japanese national dental hygienist examination, focusing on textual information only. Materials and methods: We analyzed 73 questions from the 32nd Japanese national dental hygienist examination, conducted in March 2023, using LLMs ChatGPT-3.5, GPT-4, Bard, and Bing Chat. Each question was categorized into one of nine domains. Standardized prompts were used for all LLMs, and Fisher's exact test was applied for statistical analysis. Results: GPT-4 achieved the highest accuracy (75.3%), followed by Bing (68.5%), Bard (66.7%), and GPT-3.5 (63.0%). There were no statistically significant differences between the LLMs. The performance varied across different question categories, with all models excelling in the 'Disease mechanism and promotion of recovery process' category (100% accuracy). GPT-4 generally outperformed other models, especially in multi-answer questions. Conclusion: GPT-4 demonstrated the highest overall accuracy among the LLMs tested, indicating its superior potential as an educational support tool in dental hygiene studies. The study highlights the varied performance of different LLMs across various question categories. While GPT-4 is currently the most effective, the capabilities of LLMs in educational settings are subject to continual change and improvement.
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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.