How reliable are large language models in pediatric dentistry? Performance of seven artificial intelligence models on dental specialty examination questions (2012–2024)
Year: 2026
Type: article
Abstract: BACKGROUND: Large language models (LLMs) are increasingly being used in dental education; however, their reliability in the field of pediatric dentistry remains unclear. This study aimed to compare the accuracy rates of seven different LLMs in answering pediatric dentistry questions from the Turkish Dental Specialty Examination (DSE). METHODS: A cross-sectional evaluation was conducted on a total of 174 multiple-choice pediatric dentistry questions derived from 18 DSE administered between 2012 and 2024. The questions were categorized into 13 subdomains and further classified as clinical/case-based or theoretical/knowledge-based. The performance of seven different LLMs (ChatGPT-4.1 Pro, ChatGPT-5 Pro, Claude Pro, Copilot Pro, DeepSeek-R1, Gemini Advanced 2.5 Experimental, and Perplexity Pro) was assessed. Accuracy rates were compared across models, subdomains, and question types, and factors predicting correct responses were identified using multivariate logistic regression analysis. RESULTS: Accuracy rates differed significantly among the models (p = 0.019). The highest accuracy was observed with Gemini Advanced 2.5 Experimental (93.7%), whereas the lowest was with Claude Pro (82.8%). Accuracy rates also varied significantly across subdomains (p < 0.001), with lower performance observed in Restorative treatments/materials (75.5%) and Fluoride/preventive applications (78.6%). Clinical questions were answered with higher accuracy than theoretical questions (93.7% vs. 86.2%). In the regression analysis, the Gemini Advanced 2.5 Experimental model and clinical question type were identified as independent predictors of correct responses compared with ChatGPT-5 Pro. CONCLUSION: LLMs demonstrated high performance in answering pediatric dentistry questions derived from the Turkish DSE; however, these findings should be interpreted in light of the standardized, examination-based nature of the assessment. LLMs should be used as complementary tools in pediatric dentistry education and similar domain-specific educational contexts, rather than as substitutes for expert clinical judgment.
Source: BMC Medical Education
Institutions: Erciyes University, Nuh Naci Yazgan University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W7203709270