*Result*: Can artificial intelligence chatbots think like dentists? A comparative analysis based on dental specialty examination questions in restorative dentistry.

Title:
Can artificial intelligence chatbots think like dentists? A comparative analysis based on dental specialty examination questions in restorative dentistry.
Authors:
Haberal M; Department of Restorative Dentistry, Faculty of Dentistry, Kırıkkale University, Kırıkkale, Turkey. mervehaberal@hotmail.com., Hançerlioğulları D; Department of Endodontics, Faculty of Dentistry, Kırıkkale University, Kırıkkale, Turkey.
Source:
BMC oral health [BMC Oral Health] 2026 Jan 06; Vol. 26 (1), pp. 231. Date of Electronic Publication: 2026 Jan 06.
Publication Type:
Journal Article; Comparative Study
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 101088684 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6831 (Electronic) Linking ISSN: 14726831 NLM ISO Abbreviation: BMC Oral Health Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : BioMed Central, [2001-
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Contributed Indexing:
Keywords: Artificial intelligence; Chatbots; Dental education; Dental specialty exam; Restorative dentistry
Entry Date(s):
Date Created: 20260106 Date Completed: 20260204 Latest Revision: 20260206
Update Code:
20260206
PubMed Central ID:
PMC12870005
DOI:
10.1186/s12903-025-07612-9
PMID:
41495725
Database:
MEDLINE

*Further Information*

*Background: The integration of artificial intelligence (AI) in healthcare and medical education has advanced rapidly, with conversational AI systems gaining attention for their potential in academic assessment and clinical reasoning. This study aimed to evaluate AI chatbots' performance on restorative dentistry questions from the Turkish Dental Specialty Examination (DUS), a high-stakes national exam assessing theoretical and clinical knowledge.
Methods: An in silico, cross-sectional, comparative design was employed. A total of 190 multiple-choice questions (MCQs) from 19 DUS sessions between 2012 and 2025 were obtained from the Assessment, Selection, and Placement Center (ÖSYM) website. After excluding annulled items, 188 questions were analyzed. Eight AI chatbots (ChatGPT-3.5, ChatGPT-4o Free, ChatGPT-4o Plus, Claude Sonnet 4, Microsoft Copilot, DeepSeek, Gemini 1.5, and Gemini Advanced) were tested using a standardized single-attempt protocol in Turkish. Performance measures included accuracy, response length, and response time. Questions were categorized by year, content domain, and length for subgroup analyses. Statistical analyses were conducted in Python using standard libraries. Descriptive statistics and Pearson's correlation were calculated, while comparisons involved the Shapiro-Wilk test, Levene's test, Kruskal-Wallis test, and Dunn's post hoc test, with significance set at p < 0.05.
Results: No significant difference was found in overall accuracy (p = 0.18). However, response time and word count differed significantly (p < 0.001). Gemini Advanced showed the highest accuracy (96.28%), followed by ChatGPT-4o Plus (93.62%). Gemini 1.5 produced the longest yet fastest responses, while DeepSeek had the lowest accuracy and slowest responses. Accuracy remained stable across years but varied by topic, with lower performance in complex areas such as cavity preparation. In case-based questions, Gemini Advanced, Gemini 1.5, and ChatGPT-4o Plus achieved 100% accuracy. Performance in image-based questions was inconsistent, underscoring limitations in visual reasoning.
Conclusions: AI chatbots demonstrated high accuracy in answering restorative dentistry exam questions, with Gemini Advanced, ChatGPT-4o Plus, and Gemini 1.5 showing superior performance. Despite differences in response time and content length, their potential as supplementary tools in dental education is evident, warranting further validation across specialties and contexts.
Trial Registration: Not applicable.
(© 2026. The Author(s).)*

*Declarations. Ethics approval and consent to participate: Not applicable. Open-source public data was used in this study. Consent for publication: Not applicable since there was no direct human contact. Competing interests: The authors declare no competing interests.*