ArticleBMC oral health2026
Performance of AI chatbots in responding to patients' frequently asked questions about laminate veneer restorations.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Authors and funding
2 authors.
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Abstract
backgroundAlthough AI (artificial intelligence) chatbot systems are increasingly used to obtain dental information, limited evidence exists regarding the reliability and readability of their responses in laminate veneer-related patient inquiries. The aim of the study is to assess the reliability, quality, and readability of the responses given by 3 different artificial intelligence chatbot systems to frequently asked questions about laminate veneer restorations by patients.
methodsTwenty-five frequently asked questions about laminate veneer restorations submitted to ChatGPT 5.2, Gemini 3, and DeepSeek-V3.2. AI chatbots to generate answers. The quality of these answers was assessed by two different experts using GQS (Global Quality Scale), reliability using modified DISCERN, and readability using FRES (Flesch Reading Ease Score) and FKGL (Flesch-Kincaid Grade Level). Normality was assessed with the Shapiro-Wilk test. Depending on data distribution, repeated-measures ANOVA or the Friedman test was used for comparisons among chatbots. Dunn's test was applied for post hoc analyses. A p-value < 0.05 was considered statistically significant.
resultsNo statistically significant differences were found among chatbot systems in terms of DISCERN, FRES, or FKGL scores (P > 0.05). Interobserver agreement for DISCERN was generally poor, with only Gemini demonstrating statistically significant agreement (ICC = 0.393; P = 0.023). Although a significant overall difference was observed in GQS scores (P = 0.015), no significant pairwise differences were detected. Readability analysis revealed that all chatbot responses exceeded the recommended 6th-8th grade reading level for patient education materials, with mean FKGL values corresponding to approximately 1-2 grade levels above the recommended upper limit. In addition, FRES values indicated that the generated content generally corresponded to a difficult reading level.
conclusionThe evaluated AI chatbot systems demonstrated largely comparable performance in responding to laminate veneer-related inquiries. While overall content quality was high, limitations in reliability and elevated readability levels indicate that AI-generated responses should be interpreted cautiously and used as supportive tools rather than substitutes for professional clinical guidance.
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Registered trials
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