The Performance of GPT-3.5, GPT-4, and Bard on the Japanese National Dentist Examination: A Comparison Study

Abstract

Purpose This study aims to evaluate the performance of three large language models (LLMs), the Generative Pre-trained Transformer (GPT)-3.5, GPT-4, and Google Bard, on the 2023 Japanese National Dentist Examination (JNDE) and assess their potential clinical applications in Japan. Methods A total of 185 questions from the 2023 JNDE were used. These questions were categorized by question type and category. McNemar's test compared the correct response rates between two LLMs, while Fisher's exact test evaluated the performance of LLMs in each question category. Results The overall correct response rates were 73.5% for GPT-4, 66.5% for Bard, and 51.9% for GPT-3.5. GPT-4 showed a significantly higher correct response rate than Bard and GPT-3.5. In the category of essential questions, Bard achieved a correct response rate of 80.5%, surpassing the passing criterion of 80%. In contrast, both GPT-4 and GPT-3.5 fell short of this benchmark, with GPT-4 attaining 77.6% and GPT-3.5 only 52.5%. The scores of GPT-4 and Bard were significantly higher than that of GPT-3.5 (p<0.01). For general questions, the correct response rates were 71.2% for GPT-4, 58.5% for Bard, and 52.5% for GPT-3.5. GPT-4 outperformed GPT-3.5 and Bard (p<0.01). The correct response rates for professional dental questions were 51.6% for GPT-4, 45.3% for Bard, and 35.9% for GPT-3.5. The differences among the models were not statistically significant. All LLMs demonstrated significantly lower accuracy for dentistry questions compared to other types of questions (p<0.01). Conclusions GPT-4 achieved the highest overall score in the JNDE, followed by Bard and GPT-3.5. However, only Bard surpassed the passing score for essential questions. To further understand the application of LLMs in clinical dentistry worldwide, more research on their performance in dental examinations across different languages is required.

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