ArticleOrthodontics & craniofacial research2025
Performance of AI-Chatbots to Common Temporomandibular Joint Disorders (TMDs) Patient Queries: Accuracy, Completeness, Reliability and Readability.
Article in Orthodontics & craniofacial research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Generative Artificial Intelligence-driven orthodontic education practices.BMC medical education · 2026Review
- Artificial intelligence-powered chatbots' responses to orthodontic questions from the dentistry specialization examination: Accuracy and source evaluation.Journal of dental sciences · 2026Article
- Diagnostic Performance of ChatGPT-4o in Analyzing Oral Mucosal Lesions: A Comparative Study with Experts.Medicina (Kaunas, Lithuania) · 2025Article
- Reliability of Large Language Model-Based Chatbots Versus Clinicians as Sources of Information on Orthodontics: A Comparative Analysis.Dentistry journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
TMDs are a common group of conditions affecting the temporomandibular joint (TMJ) often resulting from factors like injury, stress or teeth grinding. This study aimed to evaluate the accuracy, completeness, reliability and readability of the responses generated by ChatGPT-3.5, -4o and Google Gemini to TMD-related inquiries. Forty-five questions covering various aspects of TMDs were created by two experts and submitted by one author to ChatGPT-3.5, ChatGPT-4 and Google Gemini on the same day. The responses were evaluated for accuracy, completeness and reliability using modified Likert scales. Readability was analysed with six validated indices via a specialised tool. Additional features, such as the inclusion of graphical elements, references and safeguard mechanisms, were also documented and analysed. The Pearson Chi-Square and One-Way ANOVA tests were used for data analysis. Google Gemini achieved the highest accuracy, providing 100% correct responses, followed by ChatGPT-3.5 (95.6%) and ChatGPT-4o (93.3%). ChatGPT-4o provided the most complete responses (91.1%), followed by ChatGPT-03 (64.4%) and Google Gemini (42.2%). The majority of responses were reliable, with ChatGPT-4o at 93.3% 'Absolutely Reliable', compared to 46.7% for ChatGPT-3.5 and 48.9% for Google Gemini. Both ChatGPT-4o and Google Gemini included references in responses, 22.2% and 13.3%, respectively, while ChatGPT-3.5 included none. Google Gemini was the only model that included multimedia (6.7%). Readability scores were highest for ChatGPT-3.5, suggesting its responses were more complex than those of Google Gemini and ChatGPT-4o. Both ChatGPT-4o and Google Gemini demonstrated accuracy and reliability in addressing TMD-related questions, with their responses being clear, easy to understand and complemented by safeguard statements encouraging specialist consultation. However, both platforms lacked evidence-based references. Only Google Gemini incorporated multimedia elements into its answers.
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Registered trials
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