With advancements in artificial intelligence (AI), its role in education and examinations is expanding. This study aimed to evaluate the accuracy and response capability of ChatGPT-4o in solving questions from the Japanese National Dental Technician Examination (JNDTE). By analysing the AI's answers to a variety of questions, including both text-based items and those containing visual information across multiple domains, we examined the practicality of using AI as a supportive tool for learning and assessment in dental education. A total of 400 JNDTE questions (2018-2022) were analysed using ChatGPT-4o. Standardized prompts were used, and statistical analysis was conducted using Fisher's exact test. The AI demonstrated high accuracy in basic knowledge-based questions but lower accuracy in specialized topics requiring technical skills. Notably, accuracy decreased when visual elements (figures, tables, and photographs) were present. The highest accuracy was observed in 'Dental materials, laboratory equipment, and processing technology' (84.1%), while the lowest was in 'Orthodontic technology' (46.2%) and 'Pediatric dental technology' (50.0%). The AI particularly struggled with interpreting radiographic images and procedural workflows. ChatGPT-4o demonstrated reasonable proficiency with JNDTE-related knowledge, suggesting its potential application as a supplementary tool in dental education. However, its limitations in processing visual and technical content highlight the need for advancements in AI-driven image recognition and simulation-based learning. The findings of this study highlight the potential application of AI models like ChatGPT-4o as a supplementary tool in dental technician education and examination preparation. Enhancing AI-driven image recognition and simulation-based learning could improve its effectiveness in dental education and clinical training, ultimately contributing to better learning outcomes and skill development for future dental technicians.

Large language models (LLMs) are increasingly used in professional examinations, but their relative performance on dental board-style questions remains unclear. This study compared two reasoning-optimized models, GPT-o3 and GPT-5T, with a general-purpose multimodal model, GPT-4o, using 399 Japanese dental board-style multiple-choice questions from 2018 to 2022. All questions were presented in Japanese, and items originally accompanied by charts, photographs, or other figures were analyzed separately from items without visual materials. Accuracy and item-level agreement were assessed using pairwise McNemar tests, stratified analyses according to the original presence of visual materials, the Breslow–Day test for homogeneity of odds ratios, and two-proportion z-tests. GPT-5T achieved the highest overall accuracy (294/399, 73.7%), followed by GPT-o3 (257/399, 64.4%) and GPT-4o (255/399, 63.9%). Pairwise McNemar tests showed that GPT-5T outperformed both GPT-4o (Holm-adjusted p = 0.00098) and GPT-o3 (Holm-adjusted p = 0.00072), whereas GPT-o3 and GPT-4o did not differ significantly (Holm-adjusted p = 0.920). Accuracy was lower for questions originally containing visual materials than for questions without such materials across all three models (GPT-4o: 49.7% vs. 72.2%; GPT-o3: 55.1% vs. 69.8%; GPT-5T: 59.9% vs. 81.8%). The advantage of GPT-5T was more evident in questions without visual materials, and heterogeneity across question formats was observed for GPT-5T versus GPT-o3. GPT-5T showed the strongest performance in this dataset. Questions originally containing visual materials were associated with lower accuracy across all models. Because the comparison was based on distinct item groups rather than experimentally manipulated visual conditions, this result should be interpreted as a difference across question formats and may also reflect differences in item composition and difficulty between the two groups.

There is a close relationship between the dental field and the cognitive function of the brain. The aim of this research was to examine the effect of periodontal initial treatment on brain functional activity. The subjects were 16 patients with periodontitis. After informed consent was obtained, periodontal initial treatment included brushing instruction, scaling and root planing. No occlusal adjustments were conducted. Periodontal examination and functional magnetic resonance imaging (fMRI) were performed at the initial visits and subsequent reassessments. Periodontal tissue improved significantly after periodontal initial treatment. From fMRI testing, during both the language and calculation task, brain activity in the dorsolateral prefrontal cortex and Wernicke's area were observed to have increased compared to the initial visit. Periodontal disease causes functional impairment of working memory and selective attention. These findings suggested that the periodontal initial treatment aid in restoring brain function.

Large language models (LLMs) show varying accuracy on professional examinations, particularly for questions involving visual materials. This study compared GPT-4o, o3, GPT-5 Thinking, and Gemini 3 on 400 Japanese National Dental Technician Examination questions from 2018–2022 and investigated whether between-model differences varied by question format. The dataset comprised 252 text-only questions and 148 questions accompanied by visual materials. Accuracy was compared using Cochran’s Q test and Holm-adjusted exact McNemar tests. A question-level mixed-effects logistic regression assessed model-by-item-type interactions while accounting for examination year. Overall accuracy was 63.8% for GPT-4o, 70.8% for o3, 74.8% for GPT-5 Thinking, and 80.5% for Gemini 3 (Cochran’s Q, p < 0.001). Gemini 3 significantly outperformed the other models, whereas o3 and GPT-5 Thinking did not differ significantly after adjust-ment. All models showed lower accuracy on visual-material questions than on text-only questions. The overall model-by-item-type interaction was significant (likelihood-ratio χ² = 11.756, p = 0.008), indicating that between-model differences varied by question format. The relative advantages of GPT-5 Thinking and Gemini 3 over GPT-4o were at-tenuated for visual-material questions. These findings highlight format-dependent dif-ferences in LLM performance and the limitations of aggregate accuracy as a bench-marking metric.