Journals / Anatolian Current Medical Journal / 2026 / Volume: 8 Issue: 6

Can large language models adhere to clinical guidelines in tooth avulsion management? A repeated-measures comparative study

Banu Çiçek Tez Yaşar(Department of Pediatric Dentistry, Faculty of Dentistry, Ankara Medipol University)0000-0002-6053-5547
Meltem Karahan(Department of Pediatric Dentistry, Hamidiye Faculty of Dentistry, University of Health Sciences)0000-0001-5178-6849
Arif Yiğit Güler(Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Ankara Medipol University)0000-0002-9962-4926
Başak Eliaçık(Department of Pediatric Dentistry, Hamidiye Faculty of Dentistry, University of Health Sciences)0000-0003-1848-3007
Pages
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Publication Date
2026-10-25
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The overall intermodel difference was significant (p=0.021), with a significant pairwise difference only between ChatGPT and Copilot (p=0.015).

📌 3 Key Takeaways

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Objective & Hypothesis:Aims: To comparatively evaluate adherence to the 2020 International Association of Dental Traumatology (IADT) guidelines and short-term temporal consistency across three large language models (LLMs)-ChatGPT (GPT-5.5), Gemini 2.5 Flash, and Microsoft Copilot-when answering open-ended questions on tooth avulsion management.
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Key Findings & Data:Results: Among 378 responses, ChatGPT generated the highest number of correct responses (94/126; 74.6%), followed by Gemini (87/126; 69.0%) and Copilot (79/126; 62.7%).
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Conclusion & Impact:The overall intermodel difference was significant (p=0.021), with a significant pairwise difference only between ChatGPT and Copilot (p=0.015).
🔬Design:Modeling & Simulation
📍Scope:2020 Verileri
⚙️Variables:Artificial intelligence · large language models · tooth avulsion

Abstract

Aims: To comparatively evaluate adherence to the 2020 International Association of Dental Traumatology (IADT) guidelines and short-term temporal consistency across three large language models (LLMs)-ChatGPT (GPT-5.5), Gemini 2.5 Flash, and Microsoft Copilot-when answering open-ended questions on tooth avulsion management. Methods: Fourteen open-ended questions were categorized into four domains: Emergency and first aid, clinical management, prognosis and critical factors, and advanced decision-making. Each question was submitted to each LLM three times daily for three consecutive days. Two pediatric dentists classified responses as correct or incorrect by consensus using a guideline-based answer key. Cochran’s Q and post hoc McNemar tests were used for comparisons. Results: Among 378 responses, ChatGPT generated the highest number of correct responses (94/126; 74.6%), followed by Gemini (87/126; 69.0%) and Copilot (79/126; 62.7%). The overall intermodel difference was significant (p=0.021), with a significant pairwise difference only between ChatGPT and Copilot (p=0.015). No significant differences were observed in emergency and first aid, clinical management, or prognosis and critical factors (p>0.05). Advanced decision-making differed significantly among models (p=0.008), particularly between ChatGPT and Gemini (p=0.016). No significant within-day or across-day variation was detected (p>0.05). Conclusion: The evaluated LLMs showed no statistically significant short-term temporal variation, but their adherence to IADT recommendations varied across models and clinical domains. ChatGPT achieved the highest performance, while the greatest divergence occurred in the predefined Advanced Decision-Making domain, specifically in questions concerning extra-alveolar dry time and delayed replantation. These models should be used only as adjunctive tools, with recommendations verified against guidelines under professional supervision.

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Cite This Article

Yaşar, B. Ç. T., Karahan, M., Güler, A. Y., Eliaçık, B. (2026). Can large language models adhere to clinical guidelines in tooth avulsion management? A repeated-measures comparative study. Anatolian Current Medical Journal, Volume: 8 Issue: 6. https://ajindex.com/articles/1667864

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