ArticleJournal of dental sciences2026
Artificial intelligence-powered chatbots' responses to orthodontic questions from the dentistry specialization examination: Accuracy and source evaluation.
Article in Journal of dental sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: /purpose: The use of artificial intelligence (AI) powered chatbots in dental education is becoming increasingly widespread. Evaluating their performance and the reliability of their sources is essential to understand their educational value. The aim of this study was to evaluate the performance of AI-powered chatbots in addressing orthodontic questions from the Dental Specialty Exam (DUS) and to assess the accuracy and reliability of the information sources on which they rely. Materials and methods: A total of 129 orthodontic questions from the exam administered between 2012 and 2021 were categorized according to Bloom's taxonomy. Each question was individually entered into ChatGPT-5, Claude 3.7, and Copilot, and their performances were comparatively evaluated. The sources referenced by the chatbots while generating their answers were also assessed. The data were analyzed using Pearson's chi-squared test. Results: ChatGPT-5, Claude 3.7, and Copilot achieved accuracy rates of 82.2 %, 83.7 %, and 85.3 %, respectively. Copilot performed best on scenario-based questions (100 %) but performed worst on visual analysis questions (33.3 %). Citation analysis showed that, ChatGPT-5.0 used reliable academic sources, whereas Claude cited few and less credible references, and Copilot relied mainly on moderately reliable materials. Conclusion: Chatbots exhibited strong text-based reasoning abilities but limited visual interpretation skills. While ChatGPT-5.0 provided more reliable and well-referenced responses, other models showed weaker citation practices. These underscored both the potential and the current limitations of AI-based systems in orthodontic education and clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
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.