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Abstract

Background

The 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).

Methods

A 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.

Results

The 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%).

Conclusion

Copilot 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.

Details

1009240
Business indexing term
Company / organization
Title
Exploring the potential of artificial intelligence chatbots in prosthodontics education
Publication title
Volume
25
Pages
1-8
Publication year
2025
Publication date
2025
Section
Research
Publisher
Springer Nature B.V.
Place of publication
London
Country of publication
Netherlands
e-ISSN
14726920
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-02-27
Milestone dates
2025-01-03 (Received); 2025-02-10 (Accepted); 2025-02-27 (Published)
Publication history
 
 
   First posting date
27 Feb 2025
ProQuest document ID
3175400603
Document URL
https://www.proquest.com/scholarly-journals/exploring-potential-artificial-intelligence/docview/3175400603/se-2?accountid=208611
Copyright
© 2025. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-03-09
Database
ProQuest One Academic