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Exploring the potential of artificial intelligence chatbots in prosthodontics education

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Publicado en:BMC Medical Education vol. 25 (2025), p. 1
Autor principal: Eraslan, Ravza
Otros Autores: Ayata, Mustafa, Yagci, Filiz, Albayrak, Haydar
Publicado:
Springer Nature B.V.
Materias:
Google DeepMind OpenAI
Turkey
San Francisco California
United States > US
Prosthodontics
Language
Search engines
Accuracy
Chatbots
Materials science
Artificial intelligence
Hypotheses
Clinical decision making
Access to information
Dentures
Natural language processing
Dentistry
Large language models
Education
Decision Support Systems
Educational Resources
Learning Processes
Test Preparation
Dental Evaluation
Science Materials
Language Processing
Low Achievement
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1472-6920
DOI:10.1186/s12909-025-06849-w
Fuente:Healthcare Administration Database