ArticleClinical oral investigations2024
Performance of large language artificial intelligence models on solving restorative dentistry and endodontics student assessments.
Article in Clinical oral investigations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis.International dental journal · 2026Pooled it
- DentalEdu-AI graph: mapping evidence-grounded and knowledge-structured AI for dental education, assessment, and accuracy.The Saudi dental journal · 2026Article
- Accuracy and Temporal Consistency of ChatGPT and Gemini in Responding to Textbook and Patient-Oriented Dental Bleaching Questions: A Multi-Session Comparative Study.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 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
- Large Language Models for Endodontic Symptom Assessment and Treatment Planning Using Image-Free Clinical Records: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Performance of large language models on undergraduate endodontic multiple-choice questions.BMC oral health · 2026Article
- Large language models and generative artificial intelligence in endodontics: a scoping review.Odontology · 2026Review
- Evaluating large language models for interpreting and applying caries guidelines in clinical decision support.BMC oral health · 2026Article
- Evaluating large language models for endodontic case difficulty assessment: accuracy, consistency, temporal stability, and agreement.BMC oral health · 2026Article
- Are AI chatbots ready for endodontics? Evaluating their validity, consistency, and readability in patient-oriented responses.Odontology · 2026Article
- International Perspectives on Digital and Generative AI Adoption and Governance in Undergraduate Dental Education: A Cross-Sectional Survey.Dentistry journal · 2026Article
- Comparative performance of ChatGPT, Gemini, and Deepseek on endodontic exam questions in Turkish and English.BMC oral health · 2026Article
- Evaluation of the performance of four different large language models (ChatGPT, DeepSeek, Copilot, and Gemini) in answering oral, and maxillofacial radiology questions: pilot study.BMC oral health · 2026Article
- Generative Artificial Intelligence: Applications and Future Prospects in Dentistry.International dental journal · 2026Review
- A comparative analysis of the performance of large Language models in the dentistry specialty examination.Scientific reports · 2026Article
- Comparative performance evaluation of ChatGPT-4 Omni and Gemini Advanced in the Turkish Dentistry Specialization Exam.BMC medical education · 2026Article
- Can artificial intelligence chatbots think like dentists? A comparative analysis based on dental specialty examination questions in restorative dentistry.BMC oral health · 2026Article
- Large language models as data-driven engines for benchmarking preventive and clinical knowledge in Chinese dental examinations.Frontiers in oral health · 2026Article
- Performance of ChatGPT-4o, Gemini Advanced-2.0, and DeepSeek-V3 in the diagnosis and management of temporomandibular disorders.BMC oral health · 2025Article
- A Scoping Review of Large Language Models in Dental Education: Applications, Challenges, and Prospects.International dental journal · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe advent of artificial intelligence (AI) and large language model (LLM)-based AI applications (LLMAs) has tremendous implications for our society. This study analyzed the performance of LLMAs on solving restorative dentistry and endodontics (RDE) student assessment questions. MATERIALS AND
methods151 questions from a RDE question pool were prepared for prompting using LLMAs from OpenAI (ChatGPT-3.5,-4.0 and -4.0o) and Google (Gemini 1.0). Multiple-choice questions were sorted into four question subcategories, entered into LLMAs and answers recorded for analysis. P-value and chi-square statistical analyses were performed using Python 3.9.16.
resultsThe total answer accuracy of ChatGPT-4.0o was the highest, followed by ChatGPT-4.0, Gemini 1.0 and ChatGPT-3.5 (72%, 62%, 44% and 25%, respectively) with significant differences between all LLMAs except GPT-4.0 models. The performance on subcategories direct restorations and caries was the highest, followed by indirect restorations and endodontics.
conclusionsOverall, there are large performance differences among LLMAs. Only the ChatGPT-4 models achieved a success ratio that could be used with caution to support the dental academic curriculum. CLINICAL RELEVANCE: While LLMAs could support clinicians to answer dental field-related questions, this capacity depends strongly on the employed model. The most performant model ChatGPT-4.0o achieved acceptable accuracy rates in some subject sub-categories analyzed.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.