ArticleSurgical and radiologic anatomy : SRA2025
Evaluation of the performance of different large language models on head and neck anatomy questions in the dentistry specialization exam in Turkey.
Article in Surgical and radiologic anatomy : SRA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Effectiveness of ChatGPT-assisted and drawing-based learning in enhancing understanding of intraoral radiographic anatomy among dental students: a comparative study.BMC oral health · 2026Trial
- Performance of Seven Large Language Models on Anatomy Examination Questions.Clinical anatomy (New York, N.Y.) · 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 of large language models on undergraduate endodontic multiple-choice questions.BMC oral health · 2026Article
- Cognitive-level analysis of dentomaxillofacial radiology questions in the Turkish dentistry specialization examination: a Bloom's revised taxonomy analysis.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 of large language models for patient-oriented support in dental trauma emergencies.BMC oral health · 2026Article
- Effect of language difference and time on the accuracy of artificial intelligence chatbots responses to questions about vital pulp therapy.BMC oral health · 2026Article
- The effectiveness of large language models in dental specialty questions: a comparative study in the field of prosthodontics.BMC medical education · 2026Article
- A comparative analysis of the performance of large Language models in the dentistry specialty examination.Scientific reports · 2026Article
- Comparative performance evaluation of ChatGPT-4 Omni and Gemini Advanced in the Turkish Dentistry Specialization Exam.BMC medical education · 2026Article
- Article
- Performance comparison of large language models on pediatric dentistry questions in the Turkish dentistry specialization examination.BMC medical education · 2025Article
- From accuracy to robustness: a comparative study of five advanced large language models on the Chinese dental licensing examination.BMC oral health · 2025Article
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3 authors.
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Abstract
purposeThe aim of this study was to assess the performance of various Large Language Models (LLMs) in addressing head and neck anatomy questions from the Dental Specialization Exam (DUS), conducted between 2012 and 2021.
methodsA total of 103 multiple-choice questions were selected from DUS examinations over a decade. These questions covered major topics: Musculoskeletal System, Nervous System and Sensory Organs, Dental Anatomy, and Veins, Arteries, Lymphatic System and the Glandular System. Eight of the LLMs Gemini 1.5, Gemini 2, Copilot, Deepseek, Claude, ChatGPT 4o, ChatGPT 4, and ChatGPT o1 were employed using their most updated versions. Each model's accuracy was calculated by comparing the number of correct and incorrect responses.
resultsThe ChatGPT o1 demonstrated the highest accuracy rate among all tested models, while Gemini 1.5 showed the lowest accuracy. These differences were found to be statistically significant (p = 0.027). Post-hoc analysis revealed that the only statistically significant difference among the LLMs was between ChatGPT o1 and Gemini 1.5 (p < 0.0031). When questions were analyzed by topic, no significant accuracy differences emerged in the Musculoskeletal System section. However, the ChatGPT o1 performed best in the Nervous System and Sensory Organs category. For Dental Anatomy questions, both ChatGPT o1 and Copilot achieved top results, and for Veins, Arteries, Lymphatic System and Glandular System section, ChatGPT o1 again excelled.
conclusionOverall, the findings show that LLMs effectively answer DUS head and neck anatomy questions with comparable performance. These insights support future exam-related model development and suggest that LLMs can serve as valuable educational tools.
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