Large language models (LLMs) such as OpenAI's ChatGPT, Google's Bard, and Microsoft's Bing Chat have shown potential as educational tools in the medical and dental fields. This study evaluated their effectiveness using questions from the Japanese national dental hygienist examination, focusing on textual information only. We analyzed 73 questions from the 32nd Japanese national dental hygienist examination, conducted in March 2023, using LLMs ChatGPT-3.5, GPT-4, Bard, and Bing Chat. Each question was categorized into one of nine domains. Standardized prompts were used for all LLMs, and Fisher's exact test was applied for statistical analysis. GPT-4 achieved the highest accuracy (75.3%), followed by Bing (68.5%), Bard (66.7%), and GPT-3.5 (63.0%). There were no statistically significant differences between the LLMs. The performance varied across different question categories, with all models excelling in the ‘Disease mechanism and promotion of recovery process' category (100% accuracy). GPT-4 generally outperformed other models, especially in multi-answer questions. GPT-4 demonstrated the highest overall accuracy among the LLMs tested, indicating its superior potential as an educational support tool in dental hygiene studies. The study highlights the varied performance of different LLMs across various question categories. While GPT-4 is currently the most effective, the capabilities of LLMs in educational settings are subject to continual change and improvement.

Rapid advancements in AI technology have led to significant interest in its application across various fields, including medicine and dentistry. This study aimed to assess the capabilities of ChatGPT-4V with image recognition in answering image-based questions from the Japanese National Dental Examination (JNDE) to explore its potential as an educational support tool for dental students. The dataset used questions from the JNDE, which was conducted in January 2023, with a focus on image-related queries. ChatGPT-4V was utilized, and standardized prompts, question texts, and images were input. Data and statistical analyses were conducted using Qlik Sense® and GraphPad Prism. The overall correct response rate of ChatGPT-4V for image-based JNDE questions was 35.0 %. The correct response rates were 57.1 % for compulsory questions, 43.6 % for general questions, and 28.6 % for clinical practical questions. In specialties like Dental Anesthesiology and Endodontics, ChatGPT-4V achieved correct response rates above 70 %, while response rates for Orthodontics and Oral Surgery were lower. A higher number of images in questions was correlated with lower accuracy, suggesting an impact of the number of images on correct and incorrect responses. While innovative, ChatGPT-4V’s image recognition feature exhibited limitations, especially in handling image-intensive and complex clinical practical questions, and is not yet fully suitable as an educational support tool for dental students at its current stage. Further technological refinement and re-evaluation with a broader dataset are recommended.

Soluble tumor necrosis factor receptors 1 and 2 (sTNF-R1 and sTNF-R2) are reported to protect against excessive TNF-α, a primary mediator of systemic responses to infection. This study aimed to investigate the levels of TNF-α, sTNF-R1, and sTNF-R2 in saliva and to verify whether their dynamics are associated with periodontal health. The study population comprised 28 adult patients. Probing pocket depth, clinical attachment level, and bleeding on probing were assessed, and periodontal inflamed surface area (PISA) was calculated. Stimulated saliva was collected before the oral examinations. The levels of TNF-α, sTNF-R1, sTNF-R2, and total protein (TP) in saliva samples were determined. There were significant positive correlations between TNF-α, sTNF-R1, and sTNF-R2 to TP (/TP) in stimulated saliva. Moreover, there were significant positive correlations between PISA and sTNF-R2/TP. Stepwise multiple regression analysis revealed that PISA was significantly associated with sTNF-R2/TP in saliva; however, TNF-α/TP was not significantly associated with PISA. In conclusion, this study demonstrates that significant relationships exist between the salivary levels of TNF-α and sTNF-R1, and that salivary sTNF-R2 is associated with the expansion of inflamed periodontal tissue.

OpenAI's GPT-4V and Google's Gemini Pro, being Large Language Models (LLMs) equipped with image recognition capabilities, have the potential to be utilized in future medical diagnosis and treatment, ands serve as valuable educational support tools for students. This study compared and evaluated the image recognition capabilities of GPT-4V and Gemini Pro using questions from the Japanese National Dental Examination (JNDE) to investigate their potential as educational support tools. We analyzed 160 questions from the 116th JNDE, administered in March 2023, using ChatGPT-4V, and Gemini Pro, which have image recognition functions. Standardized prompts were used for all LLMs, and statistical analysis was conducted using Fisher's exact test and the Mann–Whitney U test. For the 160 JNDE questions, the accuracy rates of GPT-4V and Gemini Pro were 35.0% and 28.1%, respectively, with GPT-4V being the highest, although not statistically significant. Across dental specialties, the accuracy rates of the GPT-4V were generally higher than those of the Gemini Pro, with some areas showing equal accuracy. Accuracy rates tended to decrease with an increased number of images within a question, suggesting that the number of images influenced the correctness of the responses. The overall superior performance of GPT-4V compared to Gemini Pro may be attributed to the continuous updates in OpenAI's model. This research demonstrates the potential of LLMs as educational support tools in dentistry, while also highlighting areas that require further technological development.

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. 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. 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%). 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.

In Japan, medical and dental care is provided by the universal health insurance system. The Ministry of Health, Labour, and Welfare (MHLW) in Japan sets the rules for health care services provided by health insurance. The MHLW issued a notice in 2020 permitting telemedicine and dental telemedicine for the first visit and for follow-up visits to prevent the spread of COVID-19 infection. We conducted this study to clarify the status of dental telemedicine during 2020. We used data from lists obtained on the MHLW website in the analysis. We investigated the number of dental institutions conducing dental telemedicine for the first visit and for follow-up visits by prefecture. In each prefecture, fewer dental institutions conducted telemedicine for the first visit than for follow-up visits. Regions with large metropolitan areas had higher numbers of dental institutions conducting dental telemedicine for the first visit and follow-up visits. Private dental clinics provided the largest proportion of dental telemedicine for the first visit, and general hospitals provided the largest proportion for follow-up visits. Our study findings indicated that many dental institutions in Japan made efforts to provide dental services via dental telemedicine using the telephone or online with video to help prevent the spread of COVID-19 infection. Dental telemedicine can help patients to access dental services and dental care, thereby expanding the potential of dental telemedicine in Japan.