SynthesisInternational dental journal2025
Large Language Models in Dental Licensing Examinations: Systematic Review and Meta-Analysis.
Synthesis in International dental journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis.International dental journal · 2026Pooled 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
- ChatGPT-5 vs oral medicine experts for rank-based differential diagnosis of oral lesions: a prospective, biopsy-validated comparison.Odontology · 2026Article
- Benchmarking GPT-5, Gemini 2.5 Pro, Grok 4, and other LLMs on pediatric dentistry questions from a dental specialization exam.Scientific reports · 2026Article
- Validity and reliability of ChatGPT's responses on dietary supplements in Japan: A quality assessment and content analysis.PEC innovation · 2026Article
- Three large language models demonstrate competitive performance in Traditional Chinese Medicine national medical licensing examinations over two years.Scientific reports · 2026Article
- Factors influencing large language model adoption among dental students: a cross-sectional study.Scientific reports · 2026Article
- Textbook-level medical knowledge in large language models: comparative evaluation using Japanese National Medical Examination.BMC medical informatics and decision making · 2026Article
- Large Language Models and Machine Learning Framework for Predicting Dental Ceramics Performance.International dental journal · 2026Article
- Evaluation of ChatGPT's Accuracy, Repeatability, and Reasoning Ability in Prosthodontics Education: A Cross-Sectional Comparative Study with Prosthodontists.Journal of clinical and experimental dentistry · 2026Review
- A comparative analysis of the performance of large Language models in the dentistry specialty examination.Scientific reports · 2026Article
- What the performance of multimodal LLMs on a national licensing exam teaches us about occupational therapy education.BMC medical education · 2026Article
- Evaluation of GPT-4o and Gemini Advanced on the Korean National Dental Licensing Examination: Accuracy, consistency, and question generation.Journal of dental sciences · 2026Article
- Analysis of multimodal large language models on visually-based questions in the Japanese National Examination for Dental Hygienists: A preliminary comparative study.Journal of dental sciences · 2026Article
- From accuracy to robustness: a comparative study of five advanced large language models on the Chinese dental licensing examination.BMC oral health · 2025Article
- Article
- Benchmarking multimodal large language models on the dental licensing examination: Challenges with clinical image interpretation.Journal of dental sciences · 2025Article
- Analyzing the performance of multimodal large language models on visually-based questions in the Japanese National Examination for Dental Technicians.Journal of dental sciences · 2025Article
- Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
aimsThis study systematically reviews and conducts a meta-analysis to evaluate the performance of various large language models (LLMs) in dental licensing examinations worldwide. The aim is to assess the accuracy of these models in different linguistic and geographical contexts. This will inform their potential application in dental education and diagnostics.
methodsFollowing Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a comprehensive search across PubMed, Web of Science, and Scopus for studies published from 1 January 2022 to 1 May 2024. Two authors independently reviewed the literature based on the inclusion and exclusion criteria, extracted data, and evaluated the quality of the studies in accordance with the Quality Assessment of Diagnostic Accuracy Studies-2. We conducted qualitative and quantitative analyses to evaluate the performance of LLMs.
resultsEleven studies met the inclusion criteria, encompassing dental licensing examinations from eight countries. GPT-3.5, GPT-4, and Bard achieved integrated accuracy rates of 54%, 72%, and 56%, respectively. GPT-4 outperformed GPT-3.5 and Bard, passing more than half of the dental licensing examinations. Subgroup analyses and meta-regression showed that GPT-3.5 performed significantly better in English-speaking countries. GPT-4's performance, however, remained consistent across different regions.
conclusionLLMs, particularly GPT-4, show potential in dental education and diagnostics, yet their accuracy remains below the threshold required for clinical application. The lack of sufficient training data in dentistry has affected LLMs' accuracy. The reliance on image-based diagnostics also presents challenges. As a result, their accuracy in dental exams is lower compared to medical licensing exams. Additionally, LLMs even provide more detailed explanation for incorrect answer than correct one. Overall, the current LLMs are not yet suitable for use in dental education and clinical diagnosis.
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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 Socratic graph.