ArticlePloS one2025
Accuracy of latest large language models in answering multiple choice questions in dentistry: A comparative study.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Parameter-level comparison of artificial intelligence and manual cephalometric measurements: a systematic review and meta-analysis.Frontiers in dental medicine · 2026Pooled it
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Periodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations.Journal of medical Internet research · 2026Article
- Accuracy, Self-Reported Confidence, and Overconfidence of Large Language Models in Endodontics: An Evaluation Using National Specialty Examination Questions.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Accuracy of large language models in the Turkish dental specialization examination (DUS): a multidimensional evaluation across disciplines and question formats.BMC oral health · 2026Article
- Impact of guideline-based prompting on the large language model performance in dental trauma management clinical decision-making.Odontology · 2026Article
- Large Language Models for Endodontic Symptom Assessment and Treatment Planning Using Image-Free Clinical Records: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Do large language models differ in their pharmacology-related response quality for oral and maxillofacial surgery? a blinded expert benchmark study.BMC oral health · 2026Article
- Diagnostic and localization performance of multimodal large language models in the interpretation of dental periapical radiographs.BMC oral health · 2026Article
- Feasibility evaluation of large language models in anesthesia-specific post-operative care instructions for total knee arthroplasty.PEC innovation · 2026Article
- AI and 3D printing-assisted surgical exposure of impacted canines and tooth extraction in complex cleft lip and palate orthodontic treatment.Journal of surgical case reports · 2026Article
- Calibration of AI large language models with human subject matter experts for grading of clinical short-answer responses in dental education.BMC oral health · 2026Article
- A comparative analysis of the performance of large Language models in the dentistry specialty examination.Scientific reports · 2026Article
- Performance of GPT-5, DeepSeek, and Claude in dental MCQs for medically compromised patients.Journal of translational medicine · 2026Article
- Evaluating AI Chatbots in Prosthodontics Education: A Quantitative MCQ-Based Assessment.International journal of dentistry · 2026Article
- Assessment of vaccine information accuracy across large language models.Frontiers in public health · 2026Article
- Artificial intelligence-powered chatbots' responses to orthodontic questions from the dentistry specialization examination: Accuracy and source evaluation.Journal of dental sciences · 2026Article
- Assessment of artificial intelligence chatbots in responding to dental occlusion questions: a comparative study.BMC oral health · 2025Article
- From accuracy to robustness: a comparative study of five advanced large language models on the Chinese dental licensing examination.BMC oral health · 2025Article
- Quantifying the speed-accuracy trade-off of large language models on oral and maxillofacial surgery multiple-choice questions.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aims to evaluate the performance of the latest large language models (LLMs) in answering dental multiple choice questions (MCQs), including both text-based and image-based questions. MATERIAL AND
methodsA total of 1490 MCQs from two board review books for the United States National Board Dental Examination were selected. This study evaluated six of the latest LLMs as of August 2024, including ChatGPT 4.0 omni (OpenAI), Gemini Advanced 1.5 Pro (Google), Copilot Pro with GPT-4 Turbo (Microsoft), Claude 3.5 Sonnet (Anthropic), Mistral Large 2 (Mistral AI), and Llama 3.1 405b (Meta). χ2 tests were performed to determine whether there were significant differences in the percentages of correct answers among LLMs for both the total sample and each discipline (p < 0.05).
resultsSignificant differences were observed in the percentage of accurate answers among the six LLMs across text-based questions, image-based questions, and the total sample (p<0.001). For the total sample, Copilot (85.5%), Claude (84.0%), and ChatGPT (83.8%) demonstrated the highest accuracy, followed by Mistral (78.3%) and Gemini (77.1%), with Llama (72.4%) exhibiting the lowest.
conclusionsNewer versions of LLMs demonstrate superior performance in answering dental MCQs compared to earlier versions. Copilot, Claude, and ChatGPT achieved high accuracy on text-based questions and low accuracy on image-based questions. LLMs capable of handling image-based questions demonstrated superior performance compared to LLMs limited to text-based questions. CLINICAL RELEVANCE: Dental clinicians and students should prioritize the most up-to-date LLMs when supporting their learning, clinical practice, and research.
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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 OpenQuestion graph.