ArticleBMC medical education2025
Artificial intelligence in dental education: can AI-based chatbots compete with general practitioners?
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Performance of generative artificial intelligence in oral and maxillofacial radiology based on the board-certification examination of Japan.Oral radiology · 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
- Bloom's taxonomy-based comparison of artificial intelligence and dental students in restorative dentistry.BMC medical education · 2026Article
- Digital Intelligence in Dental Education: A Bibliometric Analysis.International dental journal · 2026Review
- Artificial intelligence-powered chatbots' responses to orthodontic questions from the dentistry specialization examination: Accuracy and source evaluation.Journal of dental sciences · 2026Article
- Evaluating AI Chatbots in Prosthodontics Education: A Quantitative MCQ-Based Assessment.International journal of dentistry · 2026Article
- The reliability of answers from four different AI chatbots on periodontology theoretical exam questions: an evaluation in dental education.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
backgroundThis study aimed to evaluate the performance of seven AI-based chatbots (ChatGPT-4, ChatGPT-3.5, ChatGPT 01-Preview, ChatGPT 01-Mini, Microsoft Bing, Claude, and Google Gemini) in answering multiple-choice questions related to prosthetic dentistry from the Turkish Dental Specialization Mock Exam (DUSDATA TR). Additionally, the study investigated whether these chatbots could provide responses at an accuracy level comparable to general practitioners.
methodsA total of ten multiple-choice questions related to prosthetic dentistry were selected from a preparatory exam by a private educational institution. Two groups were formed: (1) General practitioners (Human Group, N = 657) and (2) AI-based chatbots. Each question was manually input into the chatbots, and their responses were recorded. Correct responses were marked as "1" and incorrect responses as "0". The consistency and accuracy of chatbot responses were analyzed using Fisher's exact test and Cochran's Q test. Statistical significance was set at p < 0.05.
resultsA statistically significant difference was found between the accuracy rates of chatbot responses (p < 0.05). ChatGPT-3.5, ChatGPT-4, and Google Gemini failed to provide correct answers to questions 2, 5, 7, 8, and 9, while Microsoft Bing failed on questions 5, 7, 8, and 10. None of the chatbots answered question 7 correctly. General practitioners demonstrated the highest accuracy rates, particularly for question 10 (80.3%) and question 9 (44.4%). Despite variations in accuracy, chatbot responses remained consistent over time (p > 0.05). However, Bing was identified as the chatbot with the highest number of incorrect responses.
conclusionThe study findings indicate that The performance of AI-based chatbots varies significantly and lacks consistency in answering prosthetic dentistry-related exam questions, necessitating further improvement before implementation.
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Registered trials
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.