ArticleEuropean archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry2025
Evaluating the evidence-based potential of six large language models in paediatric dentistry: a comparative study on generative artificial intelligence.
Article in European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Letter to the Editor: Comment on "Evaluating the evidence-based potential of six large language models in paediatric dentistry: a comparative study on generative artificial intelligence".European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2026Article
- The Role of Large Language Models in Identifying and Correcting Paediatric Health Misinformation for Parents: A Paediatric Nursing Perspective.Journal of paediatrics and child health · 2026Article
- Prompt sensitivity of large language models in orthodontic patient counseling: a scenario-based experimental study.BMC oral health · 2026Article
- Multidimensional Evaluation of AI-Generated Patient Information on Cone Beam-Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Comparative evaluation of large language models for guideline-compliant abstract generation and readability in dental research: an experimental comparative study.Scientific reports · 2026Article
- Factors influencing large language model adoption among dental students: a cross-sectional study.Scientific reports · 2026Article
- Generative Artificial Intelligence and Large Language Models in Paediatric Dentistry: A Scoping Review.International dental journal · 2026Article
- Feasibility and exploratory assessment of large language models for pediatric dentistry queries: a comparative study.Frontiers in oral health · 2026Article
- Comparative evaluation of ChatGPT and Gemini in detecting external apical root resorption on panoramic radiographs of orthodontic patients : Artificial intelligence performance in detecting root resorption.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2025Article
- Artificial intelligence for detection and classification of furcation defects using radiographic imaging: A systematic review.Imaging science in dentistry · 2025Review
- Evaluation of the readability, quality, and accuracy of AI chatbot responses to questions about deleterious oral habits.BMC oral health · 2025Article
- Performance of ChatGPT-4 as an Auxiliary Tool: Evaluation of Accuracy and Repeatability on Orthodontic Radiology Questions.Bioengineering (Basel, Switzerland) · 2025Article
- Assessment of AI software's diagnostic accuracy in identifying impacted teeth in panoramic radiographs.European journal of orthodontics · 2025Article
- Assessing the Utility of Large Language Models in Guiding Dental Practitioners on Pediatric Patient Care: A Comparative AI Study.Journal of clinical and experimental dentistry · 2025Article
- Reliability of Large Language Model-Based Chatbots Versus Clinicians as Sources of Information on Orthodontics: A Comparative Analysis.Dentistry journal · 2025Article
- Evaluation of Large Language Model Performance in Answering Clinical Questions on Periodontal Furcation Defect Management.Dentistry journal · 2025Article
- Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Performance of large language models in fluoride-related dental knowledge: a comparative evaluation study of ChatGPT-4, Claude 3.5 Sonnet, Copilot, and Grok 3.Journal of Yeungnam medical science · 2025Article
- The evaluation of tooth whitening from a perspective of artificial intelligence: a comparative analytical study.Frontiers in digital health · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
purposeThe use of large language models (LLMs) in generative artificial intelligence (AI) is rapidly increasing in dentistry. However, their reliability is yet to be fully founded. This study aims to evaluate the diagnostic accuracy, clinical applicability, and patient education potential of LLMs in paediatric dentistry, by evaluating the responses of six LLMs: Google AI's Gemini and Gemini Advanced, OpenAI's ChatGPT-3.5, -4o and -4, and Microsoft's Copilot.
methodsTen open-type clinical questions, relevant to paediatric dentistry were posed to the LLMs. The responses were graded by two independent evaluators from 0 to 10 using a detailed rubric. After 4 weeks, answers were reevaluated to assess intra-evaluator reliability. Statistical comparisons used Friedman's and Wilcoxon's and Kruskal-Wallis tests to assess the model that provided the most comprehensive, accurate, explicit and relevant answers.
resultsVariations of results were noted. Chat GPT 4 answers were scored as the best (average score 8.08), followed by the answers of Gemini Advanced (8.06), ChatGPT 4o (8.01), ChatGPT 3.5 (7.61), Gemini (7,32) and Copilot (5.41). Statistical analysis revealed that Chat GPT 4 outperformed all other LLMs, and the difference was statistically significant. Despite variations and different responses to the same queries, remarkable similarities were observed. Except for Copilot, all chatbots managed to achieve a score level above 6.5 on all queries.
conclusionThis study demonstrates the potential use of language models (LLMs) in supporting evidence-based paediatric dentistry. Nevertheless, they cannot be regarded as completely trustworthy. Dental professionals should critically use AI models as supportive tools and not as a substitute of overall scientific knowledge and critical thinking.
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