ArticleFrontiers in oral health2026
Feasibility and exploratory assessment of large language models for pediatric dentistry queries: a comparative study.
Article in Frontiers in oral health, 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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1 citing paper in PubMed.
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8 authors.
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Abstract
Background: Large Language Models (LLMs) are increasingly used by caregivers to obtain pediatric health information. However, concerns persist regarding the accuracy, reliability, and readability of AI-generated content, especially in pediatric dentistry, where caregiver comprehension is crucial. Objective: To conduct an exploratory feasibility assessment of evaluating accuracy, quality, reliability, and readability of responses generated by ChatGPT-4, Google Gemini, and DeepSeek to common pediatric dentistry queries. Methods: This exploratory comparative cross-sectional feasibility study utilized 15 patient-oriented pediatric dentistry questions identified through structured searches and expert screening. Each question was submitted verbatim to ChatGPT-4, Gemini, and DeepSeek under standardized conditions. Responses were independently evaluated by three calibrated pediatric dentistry experts using the Global Quality Scale (GQS), a modified DISCERN tool, and the Accuracy of Information Index (AOI). Readability was assessed using the Flesch Reading Ease Score (FRES) and the Flesch-Kincaid Grade Level (FKGL). Inter-examiner reliability was assessed using intraclass correlation coefficients (ICC). Statistical comparisons between LLMs were performed using a fixed-effects model with Results: Overall scoring was consistent across examiners, with minor variability observed across domains. A linear mixed-effects model conducted separately for each domain demonstrated that LLM type significantly influenced GQS scores (F = 7.90, Conclusion: This study offers an exploratory feasibility assessment of LLM evaluation in pediatric dentistry. While the models generally produced high-quality outputs, variations in accuracy, readability, and significant inter-examiner variability highlight important methodological challenges. These findings represent preliminary groundwork and require validation in larger, clinically diverse, real-world settings. LLMs may serve as supportive informational tools; however, their outputs should be interpreted cautiously and used to complement, not replace professional clinical judgment.
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