ArticleBMC oral health2025
Artificial intelligence performance in answering multiple-choice oral pathology questions: a comparative analysis.
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 41 papers.
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
41 citing papers in PubMed.
- Effect of artificial intelligence-assisted personalized feedback on radiographic diagnostic performance of dental students: a controlled study.BMC medical education · 2025Trial
- Accuracy of large language models in the Turkish dental specialization examination (DUS): a multidimensional evaluation across disciplines and question formats.BMC oral health · 2026Article
- Accuracy and Error Patterns of References Generated by Large Language Models in Endodontics: The Role of Prompt Design and Model Selection.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Article
- Performance of large language models on undergraduate endodontic multiple-choice questions.BMC oral health · 2026Article
- JADE: jawbone lesion diagnosis and decision supporting system.Dento maxillo facial radiology · 2026Article
- Can artificial intelligence applications be utilised in the management of dental trauma? A comparison of ChatGPT, Gemini, and DeepSeek.BMC oral health · 2026Article
- Applications of DeepSeek in Medicine: Bibliometric Analysis and Scoping Review.Journal of medical Internet research · 2026Article
- Assessment of various artificial intelligence applications' performance in responding to multiple-choice endodontics questions.BMC oral health · 2026Article
- Demographic prompt cues shift clinical recommendations in multimodal large language models: a multi-model audit of 25,380 dental radiograph assessments.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
- Comparative analysis of the performance of artificial intelligence language models on Turkish dental specialty examination questions.BMC oral health · 2026Article
- Bi-linguistic performance of large language models in multimodal analysis for differentiating jawbone-destroying malignancy from osteomyelitis.BMC oral health · 2026Article
- Evaluating open-source LLMs for dental EMR generation.BMC oral health · 2026Article
- The impact of regional block presence on large language model-based postoperative analgesia recommendations in abdominal surgery: a comparative study using real-world patient data.BMC anesthesiology · 2026Observational
- Performance of 4 artificial intelligence chatbots in responding to multiple choice questions in operative dentistry.BMC oral health · 2026Article
- Comparative performance of large language models for patient-oriented support in dental trauma emergencies.BMC oral health · 2026Article
- Performance of five large language models in oral and maxillofacial surgery exam questions: a comparative study.BMC oral health · 2026Article
- Effect of language difference and time on the accuracy of artificial intelligence chatbots responses to questions about vital pulp therapy.BMC oral health · 2026Article
- The effectiveness of large language models in dental specialty questions: a comparative study in the field of prosthodontics.BMC medical education · 2026Article
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
backgroundArtificial intelligence (AI) has rapidly advanced in healthcare and dental education, significantly impacting diagnostic processes, treatment planning, and academic training. The aim of this study is to evaluate the performance differences between different large language models (LLMs) by analyzing their accuracy rates in answers to multiple choice oral pathology questions.
methodsThis study evaluates the performance of eight LLMs (Gemini 1.5, Gemini 2, ChatGPT 4o, ChatGPT 4, ChatGPT o1, Copilot, Claude 3.5, Deepseek) in answering multiple-choice oral pathology questions from the Turkish Dental Specialization Examination (DUS). A total of 100 questions from 2012 to 2021 were analyzed. Questions were classified as "case-based" or "knowledge-based". The responses were classified as "correct" or "incorrect" based on official answer keys. To prevent learning biases, no follow-up questions or feedback were provided after the LLMs' responses.
resultsSignificant performance differences were observed among the models (p < 0.001). ChatGPT o1 achieved the highest accuracy (96 correct, 4 incorrect), followed by Claude (84 correct), Gemini 2 and Deepseek (82 correct each). Copilot had the lowest performance (61 correct). Case-based questions showed notable performance variations (p = 0.034), where ChatGPT o1 and Claude excelled. For knowledge-based questions, ChatGPT o1 and Deepseek demonstrated the highest accuracy (p < 0.001). Post-hoc analysis revealed that ChatGPT o1 performed significantly better than most other models across both case-based and knowledge-based questions (p < 0.0031).
conclusionLLMs demonstrated variable proficiency in oral pathology questions, with ChatGPT o1 showing higher accuracy. LLMs shows promise as a supplementary educational tool, though further validation is required.
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.