Assessing the performance of large language models in paediatric dentistry: a study of MIH-related information quality, readability, and reliability

Background: Artificial intelligence (AI)-based chatbots are increasingly used to obtain health-related information. However, the quality, reliability, readability, and response speed of these chatbots remain uncertain in paediatric dentistry. This study compared the performance of three large language model–based chatbots in answering frequently asked questions about molar incisor hypomineralization (MIH). Methods: Twenty MIH-related questions were developed from Google Frequently Asked Questions and established clinical guidelines from the European Academy of Paediatric Dentistry (EAPD) and the American Academy of Pediatric Dentistry (AAPD). Questions were submitted to three chatbots (DeepSeek V3.1, Gemini 2.5 Pro, and ChatGPT-5). Responses were assessed using the Flesch Reading Ease Score (FRES) and Flesch–Kincaid Grade Level (FKGL), quality using the Global Quality Scale (GQS), and reliability using the modified DISCERN (mDISCERN) tool, and response time. Statistical analyses included Friedman test, Wilcoxon signed-rank test with Bonferroni correction, and Spearman correlation tests (p < 0.05). Results: Although the overall FRES comparison was significant (p = 0.025), no significant pairwise readability differences were observed after Bonferroni correction. All responses were classified as “difficult” (18–20year reading level). DeepSeek V3.1 responded faster (2.21 s) than Gemini 2.5 Pro (5.61 s) and ChatGPT-5 (6.76 s) (p = 0.006). For quality, DeepSeek V3.1 scored lower than the other models (p < 0.001), while no difference was found between Gemini 2.5 Pro and ChatGPT-5. ChatGPT-5 achieved higher mDISCERN scores than Gemini 2.5 Pro (p = 0.012), with no significant differences between ChatGPT-5 and DeepSeek V3.1 or DeepSeek V3.1 and Gemini 2.5 Pro. FRES and FKGL were strongly negatively correlated (ρ = −0.890, p < 0.001). Conclusions: AI chatbots provide similar readability for MIH-related information, their quality, reliability, and response speed differ. All models exceeded recommended readability levels, potentially limiting accessibility. Faster responses were not associated with higher quality or reliability.

Artificial intelligence; Chatbots; Molar incisor hypomineralization; Large language models

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