ArticleFrontiers in oral health2025
Performance of five free large language models in dental trauma: a 30-day longitudinal benchmark study.
Article in Frontiers in oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Impact of guideline-based prompting on the large language model performance in dental trauma management clinical decision-making.Odontology · 2026Article
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7 authors.
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Abstract
Objective: To compare the accuracy and consistency of five large language models (LLMs) in generating responses about dental trauma. Materials and methods: Sixty dichotomous (true/false) questions were submitted daily to each LLM (ChatGPT, Google Gemini, Microsoft Copilot, DeepSeek, and Meta AI) for 30 days, totaling 18,000 responses. All interactions were performed under two prompting conditions (zero-shot and zero-shot with context). LLM responses were compared against the International Association of Dental Traumatology (IADT) guidelines. Statistical analysis was conducted using a generalized linear mixed model (GLMM) with a binomial distribution ( Results: All LLMs achieved accuracy above 85%, with Microsoft Copilot (91.1%) and DeepSeek (90%) performing best; no significant difference was observed between them ( Conclusion: All evaluated LLMs, particularly Copilot and DeepSeek, demonstrated high accuracy in providing information on dental trauma, with stable performance over time. While the use of a context prompt did not significantly affect accuracy or stability.
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