Observational studyDental traumatology : official publication of International Association for Dental Traumatology2025
Can Artificial Intelligence Language Models Effectively Address Dental Trauma Questions?
Observational study in Dental traumatology : official publication of International Association for Dental Traumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of artificial intelligence in endodontic education: a systematic review.Frontiers in dental medicine · 2026Pooled it
- Evaluating ChatGPT-4o as an Educational Support Tool for the Emergency Management of Dental Trauma: Randomized Controlled Study Among Students.JMIR medical education · 2025Trial
- Evaluating Guideline Adherence in Gemini-Powered Dental Trauma Workflows: Standalone Gemini Chat vs. Document-Grounded NotebookLM.Dental traumatology : official publication of International Association for Dental Traumatology · 2026Article
- Comparative Benchmark of Seven Large Language Models for Traumatic Dental Injury Knowledge.European journal of dentistry · 2026Article
- New Frontiers in Dental Traumatology: Innovation, Prevention, and Clinical Practice.Dental traumatology : official publication of International Association for Dental Traumatology · 2026Article
- Accuracy of large language models in the Turkish dental specialization examination (DUS): a multidimensional evaluation across disciplines and question formats.BMC oral health · 2026Article
- Impact of guideline-based prompting on the large language model performance in dental trauma management clinical decision-making.Odontology · 2026Article
- SOS Dental Trauma-An Artificial Intelligence Chatbot via WhatsApp for Guiding Patients After Dental Trauma.Dental traumatology : official publication of International Association for Dental Traumatology · 2026Article
- Evaluation of Large Language Model-Based Chatbots for Dental Trauma Management: A Comparative Study Based on Accuracy, Consistency and Information Quality.Australian endodontic journal : the journal of the Australian Society of Endodontology Inc · 2026Article
- Performance Comparison of a Domain-Specific Chatbot and General-Purpose Chatbots in Dental Traumatology.Dental traumatology : official publication of International Association for Dental Traumatology · 2026Article
- Can artificial intelligence applications be utilised in the management of dental trauma? A comparison of ChatGPT, Gemini, and DeepSeek.BMC oral health · 2026Article
- ChatGPT in Pediatric Dental Trauma: A Cross-Sectional Assessment of Response Reliability to Parental Queries.Cureus · 2026Article
- AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.BMC oral health · 2026Article
- Generative Artificial Intelligence and Large Language Models in Paediatric Dentistry: A Scoping Review.International dental journal · 2026Article
- Comparative performance of large language models for patient-oriented support in dental trauma emergencies.BMC oral health · 2026Article
- AI-based decision support in pediatric dentistry: a comparative study of ChatGPT-5 and gemini advanced.BMC oral health · 2026Article
- Comparative evaluation of ChatGPT, Gemini, and Grok in clinical decision-making and general knowledge assessment for impacted maxillary canines.Korean journal of orthodontics · 2026Article
- Artificial intelligence model for application in dental traumatology.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2025Article
- Knowledge, attitudes, and practices of dental residents in managing traumatic dental injuries.BMC oral health · 2025Article
- Evaluation of the readability, quality, and accuracy of AI chatbot responses to questions about deleterious oral habits.BMC oral health · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimArtificial intelligence (AI) chatbots, also known as large language models (LLMs), have become increasingly common educational tools in healthcare. Although the use of LLMs for emergency dental trauma is gaining popularity, it is crucial to assess their reliability. This study aimed to compare the reliabilities of different LLMs in response to multiple questions related to dental trauma. MATERIALS AND
methodsIn a cross-sectional observational study conducted in October 2024, 30 questions (10 multiple-choice, 10 fill-in-the-blank, and 10 dichotomous) based on the International Association of Dental Traumatology guidelines were posed to five LLMs: ChatGPT 4, ChatGPT 3.5, Copilot Free version (Copilot F), Copilot Pro (Copilot P), and Google Gemini over nine consecutive days. Responses of each model (1350 in total) were recorded in binary format and analyzed using Pearson's chi-square and Fisher's exact tests to assess correctness and consistency (p < 0.05).
resultsThe answers provided by the LLMs to repeated questions on consecutive days showed a high degree of repeatability. Although there was no statistically significant difference in the success rate of providing correct answers among the LLMs (p > 0.05), the rankings based on the rate of successful answers were as follows: ChatGPT 3.5 (76.7%) > Copilot P (73.3%) > Copilot F (70%) > ChatGPT 4 (63.3%) > Gemini (46.7%). ChatGPT 3.5, ChatGPT 4, and Gemini showed a significantly higher correct response rate for multiple choice and fill in the blank questions compared to their performance on dichotomous (true/false) questions (p < 0.05). Conversely, The Copilot models did not exhibit significant differences across question types. Notably, the explanations provided by Copilot and Gemini were often inaccurate, and Copilot's cited references had low evidential value.
conclusionsWhile LLMs show potential as adjunct educational tools in dental traumatology, their variable accuracy and inclusion of unreliable references call for careful integration strategies.
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