ArticleThe Saudi dental journal2024
An exploratory assessment of GPT-4o and GPT-4 performance on the Japanese National Dental Examination.
Article in The Saudi dental journal, 2024. 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
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
7 citing papers in PubMed.
- Performance of Seven Large Language Models on Anatomy Examination Questions.Clinical anatomy (New York, N.Y.) · 2026Article
- Performance Comparison of a Domain-Specific Chatbot and General-Purpose Chatbots in Dental Traumatology.Dental traumatology : official publication of International Association for Dental Traumatology · 2026Article
- Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with a Structured Output (SLSO) Framework.Diagnostics (Basel, Switzerland) · 2026Article
- Benchmarking LLM decision support in inflammatory aneurysms: DeepSeek-R1, DeepSeek-V3 and ChatGPT-4o.Clinical and experimental medicine · 2026Article
- Performance of large language models on image-based oral pathology questions from the Japanese National Dental Examination.Journal of dental sciences · 2026Article
- Benchmarking multimodal large language models on the dental licensing examination: Challenges with clinical image interpretation.Journal of dental sciences · 2025Article
- Diagnostic performance of multimodal large language models in radiological quiz cases: the effects of prompt engineering and input conditions.Ultrasonography (Seoul, Korea) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objectives: Multiple large language models (LLMs) have been released since 2022, including OpenAI's GPT-3.5 and GPT-4. The latest model, GPT-4o, introduced on May 13, 2024, significantly improves GPT-4. Previous studies have shown the potential of LLMs as educational tools in medical and dental exams. This study evaluates the accuracy of GPT-4 and GPT-4o responses for the Japanese National Dental Examination (JNDE) to assess their potential as educational tools for dental education. Materials and methods: We obtained the dataset of the 117th JNDE, administered in January 2024, consisting of 360 questions. After excluding questions with images and inappropriate ones, 202 questions were selected. GPT-4 and GPT-4o were used to generate responses. Standardized prompts ensured consistent input. Data analysis used Qlik Sense® and GraphPad Prism, employing Fisher's exact test. Results: GPT-4o showed a significantly higher correct response rate (73.8%) than GPT-4 (63.3%). In the compulsory section, GPT-4o achieved 88.6% accuracy, significantly higher than GPT-4's 74.3%. Though not statistically significant, the general section saw an improvement with GPT-4o (66.4%) over GPT-4 (58.0%). Conclusion: GPT-4o significantly outperformed GPT-4 in accuracy for JNDE questions, suggesting its improved potential as an educational tool in dental education. Further studies are needed to evaluate GPT-4o's capabilities with visual materials and in diverse question sets to fully ascertain its utility in educational settings.
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.