ArticleJournal of clinical and experimental dentistry2025
Assessing the Utility of Large Language Models in Guiding Dental Practitioners on Pediatric Patient Care: A Comparative AI Study.
Article in Journal of clinical and experimental dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Assessing the accuracy, reliability, quality, and readability of artificial intelligence chatbots in patient education: insights from zirconia crowns.BMC oral health · 2026Article
- Feasibility and exploratory assessment of large language models for pediatric dentistry queries: a comparative study.Frontiers in oral health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Large Language Models (LLMs) are transforming clinical decision-making by offering rapid, context-aware access to evidence-based knowledge. However, their efficacy in pediatric dentistry remains underexplored, especially across multiple LLM platforms.Objective: To comparatively evaluate the clinical quality, readability, and originality of responses generated by nine contemporary LLMs for pediatric dental queries. Material and Methods: A cross-sectional study assessed the performance of ChatGPT-3.5, ChatGPT-4o, Gemini 2.0, Gemini 2.5, Claude 3.5 Haiku, Claude 3.7 Sonnet, Grok-3, Grok-3 Mini, and DeepSeek-V3. Twenty pediatric dental questions were posed in one-shot queries to each LLM. Responses were evaluated by ten pediatric dental experts using the Modified Global Quality Scale (MGQS), Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), and Turnitin Similarity Index. ANOVA and Cohen's Kappa were used for statistical analysis. Results: ChatGPT-4o demonstrated the highest overall MGQS (4.28 ± 0.24), followed by ChatGPT-3.5 (3.45 ± 0.27). DeepSeek-V3 scored lowest (2.18 ± 0.19). Topic-wise, ChatGPT-4o consistently outperformed others across all subdomains. FRES and FKGL scores indicated moderate readability, with Claude models exhibiting the highest linguistic complexity. Turnitin analysis revealed low-to-moderate similarity across models. Inter-rater agreement was substantial (κ = 0.78). Conclusions: Among evaluated LLMs, ChatGPT-4o exhibited superior performance in clinical relevance, coherence, and originality, suggesting its potential utility as an adjunct in pediatric dental decision-making. Nonetheless, variability across models underscores the need for critical appraisal and cautious integration into clinical workflows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.