ArticleBMC medical education2026
The effectiveness of large language models in dental specialty questions: a comparative study in the field of prosthodontics.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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3 authors.
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Abstract
objectiveThis study was aimed to compare the accuracy of four state-of-the-art large language models (LLMs)(ChatGPT-5, ChatGPT-4o, DeepSeek, and Gemini 2.5 Pro) on Dentistry Specialization Exam (DUS) questions in Prosthodontics, and to assess the effect of querying method on performance. MATERIALS AND
methodsA total of 128 multiple-choice DUS questions (106 knowledge-based; 22 case-based) from 13 exams (2012-2021) were administered. Each model was tested within a single 24-hour window (September 9, 2025). Two protocols were used: (1) Independent Query (each item in a fresh chat) and (2) Sequential Query (exam-like blocks of 10 items). First responses only were recorded and scored against official answer keys. Statistical analyses were performed using the chi-square (χ²) test to compare categorical outcomes (correct/incorrect responses) across different large language models and query strategies. Inter-rater agreement for question classification was assessed using Cohen's kappa coefficient, and all analyses were conducted at a significance level of α = 0.05.
resultsWith Independent Query, the accuracy for knowledge questions was found to be 91% (GPT-5), 86% (GPT-4o), 71% (DeepSeek), and 88% (Gemini 2.5 Pro) while total accuracy was detected as 86%, 83%, 66%, and 85% respectively. In this context, statistically significant differences were reported across models, also due to the low scores of DeepSeek. Case-based accuracies (64%, 68%, 45%, and 77%) did not differ significantly. With Sequential Query, knowledge accuracies were 75%, 73%, 63%, and 82% and case accuracies were 77%, 68%, 64%, and 91%, respectively. Total accuracy still differed across models (75%, 72%, 63%, and 84%). Within-model comparisons showed significant drops for knowledge items from Independent to Sequential querying for GPT-5 (91%→75%) and GPT-4o (86%→73%). DeepSeek and Gemini 2.5 Pro showed no significant changes. Notably, Gemini 2.5 Pro yielded the highest performance in case-based questions (91%) with sequential query.
conclusionsWhile the present study findings highlight current limitations in clinical reasoning, they support the conclusion that LLMs can be used as supplementary educational tools for DUS-style knowledge assessment but should not replace expert judgment or patient-specific decision-making.
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