ArticleBMC medical education2025
Exploring the potential of artificial intelligence chatbots in prosthodontics education.
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 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 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
- Mapping the Influence of Artificial Intelligence in Prosthodontics: A Scoping Review of the Current Trends and Challenges.Dentistry journal · 2026Review
- Quality, completeness, readability, and response time of AI chatbots in the management of deep caries and pulp exposure: a bilingual comparative study.BMC oral health · 2026Article
- Bloom's taxonomy-based comparison of artificial intelligence and dental students in restorative dentistry.BMC medical education · 2026Article
- AI Chatbots vs. Traditional Sources: Dental Health Literacy and Confidence Among Dental Patients-A Cross-Sectional Study.International dental journal · 2026Article
- Factors influencing large language model adoption among dental students: a cross-sectional study.Scientific reports · 2026Article
- Effect of language difference and time on the accuracy of artificial intelligence chatbots responses to questions about vital pulp therapy.BMC oral health · 2026Article
- The effectiveness of large language models in dental specialty questions: a comparative study in the field of prosthodontics.BMC medical education · 2026Article
- Assessing the accuracy, reliability, quality, and readability of artificial intelligence chatbots in patient education: insights from zirconia crowns.BMC oral health · 2026Article
- Can artificial intelligence chatbots think like dentists? A comparative analysis based on dental specialty examination questions in restorative dentistry.BMC oral health · 2026Article
- Evaluating AI Chatbots in Prosthodontics Education: A Quantitative MCQ-Based Assessment.International journal of dentistry · 2026Article
- Artificial intelligence-powered chatbots' responses to orthodontic questions from the dentistry specialization examination: Accuracy and source evaluation.Journal of dental sciences · 2026Article
- Artificial intelligence and its impact on oral health care education. A randomized study.Journal of education and health promotion · 2026Article
- Article
- Evaluation of AI models for radiology exam preparation: DeepSeek vs. ChatGPT-3.5.Medical education online · 2025Article
- Performance comparison of large language models on pediatric dentistry questions in the Turkish dentistry specialization examination.BMC medical education · 2025Article
- Assessment of artificial intelligence chatbots in responding to dental occlusion questions: a comparative study.BMC oral health · 2025Article
- Faculty Insights on Artificial Intelligence Chatbot Integration in Dental Implant Clinical Education.Journal of dental education · 2025Article
- A Scoping Review of Large Language Models in Dental Education: Applications, Challenges, and Prospects.International dental journal · 2025Article
- Quantifying the speed-accuracy trade-off of large language models on oral and maxillofacial surgery multiple-choice questions.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this study was to evaluate the performance of widely used artificial intelligence (AI) chatbots in answering prosthodontics questions from the Dentistry Specialization Residency Examination (DSRE).
methodsA total of 126 DSRE prosthodontics questions were divided into seven subtopics (dental morphology, materials science, fixed dentures, removable partial dentures, complete dentures, occlusion/temporomandibular joint, and dental implantology). Questions were translated into English by the authors, and this version of the questions were asked to five chatbots (ChatGPT-3.5, Gemini Advanced, Claude Pro, Microsoft Copilot, and Perplexity) within a 7-day period. Statistical analyses, including chi-square and z-tests, were performed to compare accuracy rates across the chatbots and subtopics at a significance level of 0.05.
resultsThe overall accuracy rates for the chatbots were as follows: Copilot (73%), Gemini (63.5%), ChatGPT-3.5 (61.1%), Claude Pro (57.9%), and Perplexity (54.8%). Copilot significantly outperformed Perplexity (P = 0.035). However, no significant differences in accuracy were found across subtopics among chatbots. Questions on dental implantology had the highest accuracy rate (75%), while questions on removable partial dentures had the lowest (50.8%).
conclusionCopilot showed the highest accuracy rate (73%), significantly outperforming Perplexity (54.8%). AI models demonstrate potential as educational support tools but currently face limitations in serving as reliable educational tools across all areas of prosthodontics. Future advancements in AI may lead to better integration and more effective use in dental education.
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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.