Can large language models adhere to clinical guidelines in tooth avulsion management? A repeated-measures comparative study
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.
Keywords
Supporting Institution
This research received no funding.
Ethical Statement
No ethical approval was needed because this is not a human study, but only online information was used.
References
- Glendor U, Halling A, Andersson L, Eilert-Petersson E. Incidence of traumatic tooth injuries in children and adolescents in the county of Västmanland, Sweden. Swed Dent J. 1996;20(1-2):15-28
- Andreasen JO, Andreasen FM, Tsilingaridis G. Avulsions. In: Andreasen JO, Andreasen FM, Andersson L, eds. Textbook and Color Atlas of Traumatic Injuries to the Teeth. 5th ed. Wiley-Blackwell; 2019:486-528
- Fouad AF, Abbott PV, Tsilingaridis G, et al. International Association of Dental Traumatology guidelines for the management of traumatic dental injuries: 2. Avulsion of permanent teeth. Dent Traumatol. 2020; 36(4):331-342. doi:10.1111/edt.12573
- Mustuloğlu Ş, Deniz BP. Evaluation of chatbots in the emergency management of avulsion injuries. Dent Traumatol. 2025;41(4):437-444. doi:10.1111/edt.13041
- Guven Y, Ozdemir OT, Kavan MY. Performance of Artificial Intelligence chatbots in responding to patient queries related to traumatic dental injuries: a comparative study. Dent Traumatol. 2025;41(3):338-347. doi:10.1111/edt.13020
- Tokgöz Kaplan T, Cankar M. Evidence-based potential of generative Artificial Intelligence large language models on dental avulsion: ChatGPT versus Gemini. Dent Traumatol. 2025;41(2):178-186. doi:10.1111/edt.12999
- Wang L, Wan Z, Ni C, et al. Applications and concerns of ChatGPT and other conversational large language models in health care: systematic review. J Med Internet Res. 2024;26:e22769. doi:10.2196/22769
- Ghanem YK, Rouhi AD, Al-Houssan A, et al. Dr. Google to Dr. ChatGPT: assessing the content and quality of Artificial Intelligence-generated medical information on appendicitis. Surg Endosc. 2024;38(5):2887-2893. doi:10.1007/s00464-024-10739-5
Details
Primary Language
English
Subjects
Oral and Maxillofacial Surgery, Paedodontics, Endodontics, Dental Public Health
Journal Section
Research Article
Publication Date
October 25, 2026
Submission Date
July 23, 2026
Acceptance Date
August 29, 2026
Published in Issue
Year 2026 Volume: 8 Number: 6
APA
Tez Yaşar, B. Ç., Karahan, M., Güler, A. Y., & Kızıltan Eliaçık, B. B. (2026). Can large language models adhere to clinical guidelines in tooth avulsion management? A repeated-measures comparative study. Anatolian Current Medical Journal, 8(6), 1199-1207. https://doi.org/10.38053/acmj.2002163
AMA
1.Tez Yaşar BÇ, Karahan M, Güler AY, Kızıltan Eliaçık BB. Can large language models adhere to clinical guidelines in tooth avulsion management? A repeated-measures comparative study. Anatolian Curr Med J / ACMJ / acmj. 2026;8(6):1199-1207. doi:10.38053/acmj.2002163
Chicago
Tez Yaşar, Banu Çiçek, Meltem Karahan, Arif Yiğit Güler, and Bahar Başak Kızıltan Eliaçık. 2026. “Can Large Language Models Adhere to Clinical Guidelines in Tooth Avulsion Management? A Repeated-Measures Comparative Study”. Anatolian Current Medical Journal 8 (6): 1199-1207. https://doi.org/10.38053/acmj.2002163.
EndNote
Tez Yaşar BÇ, Karahan M, Güler AY, Kızıltan Eliaçık BB (October 1, 2026) Can large language models adhere to clinical guidelines in tooth avulsion management? A repeated-measures comparative study. Anatolian Current Medical Journal 8 6 1199–1207.
IEEE
[1]B. Ç. Tez Yaşar, M. Karahan, A. Y. Güler, and B. B. Kızıltan Eliaçık, “Can large language models adhere to clinical guidelines in tooth avulsion management? A repeated-measures comparative study”, Anatolian Curr Med J / ACMJ / acmj, vol. 8, no. 6, pp. 1199–1207, Oct. 2026, doi: 10.38053/acmj.2002163.
ISNAD
Tez Yaşar, Banu Çiçek - Karahan, Meltem - Güler, Arif Yiğit - Kızıltan Eliaçık, Bahar Başak. “Can Large Language Models Adhere to Clinical Guidelines in Tooth Avulsion Management? A Repeated-Measures Comparative Study”. Anatolian Current Medical Journal 8/6 (October 1, 2026): 1199-1207. https://doi.org/10.38053/acmj.2002163.
JAMA
1.Tez Yaşar BÇ, Karahan M, Güler AY, Kızıltan Eliaçık BB. Can large language models adhere to clinical guidelines in tooth avulsion management? A repeated-measures comparative study. Anatolian Curr Med J / ACMJ / acmj. 2026;8:1199–1207.
MLA
Tez Yaşar, Banu Çiçek, et al. “Can Large Language Models Adhere to Clinical Guidelines in Tooth Avulsion Management? A Repeated-Measures Comparative Study”. Anatolian Current Medical Journal, vol. 8, no. 6, Oct. 2026, pp. 1199-07, doi:10.38053/acmj.2002163.
Vancouver
1.Banu Çiçek Tez Yaşar, Meltem Karahan, Arif Yiğit Güler, Bahar Başak Kızıltan Eliaçık. Can large language models adhere to clinical guidelines in tooth avulsion management? A repeated-measures comparative study. Anatolian Curr Med J / ACMJ / acmj. 2026 Oct. 1;8(6):1199-207. doi:10.38053/acmj.2002163
