Evidence map›Paper›PMID 42622945›Full record

ArticleOral radiology2026

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

Yohei Takeshita, Toshiyuki Kawazu, Miki Hisatomi, Mamiko Fujikura, Shunsuke Okada, Akira Shibanuma, Yuri Namba, Suzuka Yoshida, Saori Yoshida, Yoshihide Nakamura and 1 more

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Article in Oral radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yohei TakeshitaDepartment of Oral and Maxillofacial Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan. takeshita@okayama-u.ac.jp.ORCID https://orcid.org/0000-0002-9345-5797
Toshiyuki KawazuDepartment of Oral and Maxillofacial Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.
Miki HisatomiDepartment of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.
Mamiko FujikuraDepartment of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.
Shunsuke OkadaDepartment of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.
Akira ShibanumaDepartment of Community and Global Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
Yuri NambaDepartment of Oral and Maxillofacial Radiology, Division of Dentistry, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.
Suzuka YoshidaDepartment of Oral and Maxillofacial Radiology, Division of Dentistry, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.
Saori YoshidaPreliminary Examination Room, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.
Yoshihide NakamuraDepartment of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.
Yoshinobu YanagiDepartment of Oral and Maxillofacial Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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).

methodsThe 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.

resultsGPT-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.

conclusionsOn 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.

Indexed as

CertificationGenerative Artificial IntelligenceRadiologyHumansJapanArtificial intelligenceChatGPTEducationGenerative artificial intelligenceLarge language modelRadiology

Identifiers

PMID42622945
PMCPMC13619696

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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.