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DOI 10.1007/s11282-026-00965-z

Медицинская библиотека · Oral radiology · 2026

Performance of generative artificial intelligence in oral and maxillofacial radiology based on the board-certification examination of Japan.

Takeshita Yohei, Kawazu Toshiyuki, Hisatomi Miki, Fujikura Mamiko, Okada Shunsuke, Shibanuma Akira, Namba Yuri, Yoshida Suzuka, Yoshida Saori, Nakamura Yoshihide, Yanagi Yoshinobu

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Аннотация

OBJECTIVE

To evaluate the performance and potential utility of generative artificial intelligence (AI) in oral and maxillofacial radiology using the board-certification examination administered by the Japanese Society for Oral and Maxillofacial Radiology (JSOMR).

METHODS

The responses generated by ChatGPT for multiple-choice questions from the board-certification examination of the JSOMR over the three-year period from 2020 to 2022 were assessed. The questions were manually entered individually as prompts for GPT-3.5, GPT-4, and GPT-5, which are the models available from ChatGPT. The accuracy was calculated according to examination year, question format, and level of taxonomy.

RESULTS

GPT-3.5 achieved an accuracy of 40.3% for the three years (42.9%, 42.0%, and 36.0% for 2020, 2021, and 2022, respectively), that of GPT-4 was 67.8% (67.3%, 74.0%, and 62.0%, respectively), and that of GPT-5 was 76.5% (79.6%, 78.0%, and 72.0%, respectively). GPT-5's results exceeded the passing score for each year with the accuracy significantly outperformed that of GPT-3.5 and GPT-4. Regarding performance according to the question format, GPT-5 performed significantly superior to the earlier models, especially on two-answer questions.

CONCLUSIONS

On the board-certification examination of the JSOMR, the performance of GPT-5 was significantly superior to that of GPT-3.5 and GPT-4. This suggests that, given the rapid development of generative AI, GPT-5 has reached a level of text-based knowledge equivalent to that assessed in the board-certification examination of the JSOMR.

Для цитирования: Takeshita Yohei, Kawazu Toshiyuki, Hisatomi Miki, Fujikura Mamiko, Okada Shunsuke, Shibanuma Akira, Namba Yuri, Yoshida Suzuka, Yoshida Saori, Nakamura Yoshihide, Yanagi Yoshinobu. Performance of generative artificial intelligence in oral and maxillofacial radiology based on the board-certification examination of Japan.. Oral radiology. 2026. DOI: 10.1007/s11282-026-00965-z.

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