ArticleBMC oral health2025
Evaluation of the accuracy of ChatGPT-4 and Gemini's responses to the World Dental Federation's frequently asked questions on oral health.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- Accuracy and Temporal Consistency of ChatGPT and Gemini in Responding to Textbook and Patient-Oriented Dental Bleaching Questions: A Multi-Session Comparative Study.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2026Article
- Article
- Multidimensional Evaluation of AI-Generated Patient Information on Cone Beam-Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Assessing the accuracy, reliability, quality, and readability of artificial intelligence chatbots in patient education: insights from zirconia crowns.BMC oral health · 2026Article
- From accuracy to robustness: a comparative study of five advanced large language models on the Chinese dental licensing examination.BMC oral health · 2025Article
- The reliability of answers from four different AI chatbots on periodontology theoretical exam questions: an evaluation in dental education.BMC oral health · 2025Article
- The evaluation of tooth whitening from a perspective of artificial intelligence: a comparative analytical study.Frontiers in digital health · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe field of artificial intelligence (AI) has experienced considerable growth in recent years, with the advent of technologies that are transforming a range of industries, including healthcare and dentistry. Large language models (LLMs) and natural language processing (NLP) are pivotal to this transformation. This study aimed to assess the efficacy of AI-supported chatbots in responding to questions frequently asked by patients to their doctors regarding oral health.
methodsFrequently asked questions in the oral health section of the World Dental Federation FDI website were asked about Google-Gemini Trends and ChatGPT-4 chatbots on July 9, 2024. Responses from ChatGPT and Gemini, as well as those from the FDI webpage, were recorded. The accuracy of the responses given by ChatGPT-4 and Gemini to the four specified questions, the detection of similarities and differences, and the comprehensive examination of ChatGPT-4 and Gemini's capabilities were analyzed and reported by the researchers. Furthermore, the content of the texts was evaluated in terms of their similarity with respect to the following criteria: "Main Idea," "Quality Analysis," "Common Ideas," and "Inconsistent Ideas."
resultsIt was observed that both ChatGPT-4 and Gemini exhibited performance comparable to that of the FDI responses in terms of completeness and clarity. Compared with Gemini, ChatGPT-4 provided responses that were more similar to the FDI responses in terms of relevance. Furthermore, ChatGPT-4 provided responses that were more accurate than those of Gemini in terms of the "Accuracy" criterion.
conclusionsThis study demonstrated that, according to the assessment conducted by FDI, the ChatGPT-4 and Gemini applications contain contemporary and comprehensible information in response to general inquiries concerning oral health. These applications are regarded as a prevalent and dependable source of information for individuals seeking to access such data.
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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.