ArticleBMC oral health2025
ChatGPT-4 Omni's superiority in answering multiple-choice oral radiology questions.
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 30 papers, 1 of them a synthesis that pooled it.
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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.
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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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of large language models in dentomaxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Can artificial intelligence training improve clinical decision-making during deep caries excavation?Evidence-based dentistry · 2026Trial
- Assessing diagnostic performance of multimodal AI and human experts in oral and maxillofacial radiography: a comparative analysis of ChatGPT, Grok, and MANUS.Annals of medicine · 2026Article
- Can vision-language models estimate patient age? evidence from panoramic radiographs.Clinical oral investigations · 2026Article
- Article
- Quality, completeness, readability, and response time of AI chatbots in the management of deep caries and pulp exposure: a bilingual comparative study.BMC oral health · 2026Article
- Bloom's taxonomy-based comparison of artificial intelligence and dental students in restorative dentistry.BMC medical education · 2026Article
- Assessment of various artificial intelligence applications' performance in responding to multiple-choice endodontics questions.BMC oral health · 2026Article
- Cognitive-level analysis of dentomaxillofacial radiology questions in the Turkish dentistry specialization examination: a Bloom's revised taxonomy analysis.BMC oral health · 2026Article
- Multidimensional Evaluation of AI-Generated Patient Information on Cone Beam-Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Evaluating large language model`s performance in answering principles of health course questions.Scientific reports · 2026Article
- Comparative diagnostic accuracy of multiple large language models in oral and maxillofacial radiology specialty examinations: a 13-year analysis of performance and topic trends.BMC oral health · 2026Article
- Comparative performance of AI models on case-based oral medicine questions across Bloom's taxonomy levels and subtopics.Odontology · 2026Article
- Evaluation of the performance of four different large language models (ChatGPT, DeepSeek, Copilot, and Gemini) in answering oral, and maxillofacial radiology questions: pilot study.BMC oral health · 2026Article
- A comparative analysis of the performance of large Language models in the dentistry specialty examination.Scientific reports · 2026Article
- Can artificial intelligence chatbots think like dentists? A comparative analysis based on dental specialty examination questions in restorative dentistry.BMC oral health · 2026Article
- Performance of deepseek-R1 and ChatGPT-5.4 thinking in the medical laboratory professional title examination: accuracy, stability, and comparison with interns.Frontiers in digital health · 2026Article
- A Comparative Analysis of GPT-3.5, GPT-4, GPT-4 Omni, Gemini Advanced, and Gemini 1.5 in Answering Total Knee Replacement-Related Questions.Orthopaedic journal of sports medicine · 2026Article
- Performance of DeepSeek and ChatGPT on the Chinese Health Professional and Technical Examination: A comparative study.PloS one · 2026Article
- Assessment of artificial intelligence chatbots in responding to dental occlusion questions: a comparative study.BMC oral health · 2025Article
Corrections and comments
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Authors and funding
1 author.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study evaluates and compares the performance of ChatGPT-3.5, ChatGPT-4 Omni (4o), Google Bard, and Microsoft Copilot in responding to text-based multiple-choice questions related to oral radiology, as featured in the Dental Specialty Admission Exam conducted in Türkiye. MATERIALS AND
methodsA collection of text-based multiple-choice questions was sourced from the open-access question bank of the Turkish Dental Specialty Admission Exam, covering the years 2012 to 2021. The study included 123 questions, each with five options and one correct answer. The accuracy levels of ChatGPT-3.5, ChatGPT-4o, Google Bard, and Microsoft Copilot were compared using descriptive statistics, the Kruskal-Wallis test, Dunn's post hoc test, and Cochran's Q test. RESULTS AND DISCUSSION: The accuracy of the responses generated by the four chatbots exhibited statistically significant differences (p = 0.000). ChatGPT-4o achieved the highest accuracy at 86.1%, followed by Google Bard at 61.8%. ChatGPT-3.5 demonstrated an accuracy rate of 43.9%, while Microsoft Copilot recorded a rate of 41.5%.
conclusionChatGPT-4o showcases superior accuracy and advanced reasoning capabilities, positioning it as a promising educational tool. With regular updates, it has the potential to serve as a reliable source of information for both healthcare professionals and the general public. CLINICAL TRIAL NUMBER: Not applicable.
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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 Socratic graph.