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

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Title: Performance of generative artificial intelligence in oral and maxillofacial radiology based on the board-certification examination of Japan.
Authors: Takeshita Y; Department 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., Kawazu T; Department 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., Hisatomi M; Department of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan., Fujikura M; Department of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan., Okada S; Department of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan., Shibanuma A; Department of Community and Global Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan., Namba Y; Department of Oral and Maxillofacial Radiology, Division of Dentistry, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan., Yoshida S; Department of Oral and Maxillofacial Radiology, Division of Dentistry, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan., Yoshida S; Preliminary Examination Room, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan., Nakamura Y; Department 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., Yanagi Y; Department 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.; Department of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.; Department of Oral and Maxillofacial Radiology, Division of Dentistry, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.; Preliminary Examination Room, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.; Department 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.
Source: Oral radiology [Oral Radiol] 2026 Oct; Vol. 42 (4), pp. 1184-1191. Date of Electronic Publication: 2026 Aug 20.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Springer-Verlag Tokyo Country of Publication: Japan NLM ID: 8806621 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1613-9674 (Electronic) Linking ISSN: 09116028 NLM ISO Abbreviation: Oral Radiol Subsets: MEDLINE
Imprint Name(s): Publication: 2004- : Tokyo : Springer-Verlag Tokyo
Original Publication: Gifu, Japan : Japanese Society of Dental Radiology
MeSH Terms: Radiology*/education , Generative Artificial Intelligence* , Certification*, Japan ; Humans
Abstract: 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.
(© 2026. The Author(s).)
Competing Interests: Declarations. Conflicts of interest: Takeshita Y, Kawazu T, Hisatomi M, Fujikura M, Okada S, Shibanuma A, Namba Y, Yoshida S, Yoshida S, Nakamura Y, Yanagi Y declare that they have no conflict of interest. Ethics approval: This article does not contain any studies with human or animal subjects performed by the any of the authors.
References: Nat Rev Cancer. 2018 Aug;18(8):500-510. (PMID: 29777175)
Radiology. 2023 Jul;308(1):e231040. (PMID: 37462501)
Dentomaxillofac Radiol. 2024 Sep 1;53(6):390-395. (PMID: 38848473)
Radiology. 2023 Jun;307(5):e230987. (PMID: 37191491)
Radiology. 2023 Nov;309(2):e232561. (PMID: 37987662)
Radiology. 2023 Apr;307(2):e230163. (PMID: 36700838)
Radiology. 2023 Jun;307(5):e231259. (PMID: 37367439)
BMC Med Educ. 2025 Oct 2;25(1):1319. (PMID: 41039365)
BMC Med Educ. 2025 Oct 10;25(1):1403. (PMID: 41074130)
J Dent Sci. 2024 Jul;19(3):1595-1600. (PMID: 39035269)
Healthcare (Basel). 2023 Mar 19;11(6):. (PMID: 36981544)
Radiology. 2023 Jun;307(5):e230582. (PMID: 37191485)
Radiology. 2024 Jan;310(1):e232411. (PMID: 38226874)
Radiology. 2023 May;307(4):e230424. (PMID: 37014239)
Multimed Tools Appl. 2023;82(3):3713-3744. (PMID: 35855771)
Jpn J Radiol. 2024 Feb;42(2):201-207. (PMID: 37792149)
BMC Oral Health. 2025 Jul 21;25(1):1229. (PMID: 40691556)
Dentomaxillofac Radiol. 2025 Nov 1;54(8):613-631. (PMID: 40796316)
Nat Med. 2023 Aug;29(8):1930-1940. (PMID: 37460753)
Yearb Med Inform. 2022 Aug;31(1):243-253. (PMID: 35654422)
Contributed Indexing: Keywords: Artificial intelligence; ChatGPT; Education; Generative artificial intelligence; Large language model; Radiology
Entry Date(s): Date Created: 20260820 Date Completed: 20260928 Latest Revision: 20261001
Update Code: 20261001
PubMed Central ID: PMC13619696
DOI: 10.1007/s11282-026-00965-z
PMID: 42622945
Database: MEDLINE
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  Data: Performance of generative artificial intelligence in oral and maxillofacial radiology based on the board-certification examination of Japan.
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  Data: <searchLink fieldCode="AU" term="%22Takeshita+Y%22">Takeshita Y</searchLink>; Department 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.<br /><searchLink fieldCode="AU" term="%22Kawazu+T%22">Kawazu T</searchLink>; Department 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.<br /><searchLink fieldCode="AU" term="%22Hisatomi+M%22">Hisatomi M</searchLink>; Department of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.<br /><searchLink fieldCode="AU" term="%22Fujikura+M%22">Fujikura M</searchLink>; Department of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.<br /><searchLink fieldCode="AU" term="%22Okada+S%22">Okada S</searchLink>; Department of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.<br /><searchLink fieldCode="AU" term="%22Shibanuma+A%22">Shibanuma A</searchLink>; Department of Community and Global Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.<br /><searchLink fieldCode="AU" term="%22Namba+Y%22">Namba Y</searchLink>; Department of Oral and Maxillofacial Radiology, Division of Dentistry, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.<br /><searchLink fieldCode="AU" term="%22Yoshida+S%22">Yoshida S</searchLink>; Department of Oral and Maxillofacial Radiology, Division of Dentistry, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.<br /><searchLink fieldCode="AU" term="%22Yoshida+S%22">Yoshida S</searchLink>; Preliminary Examination Room, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.<br /><searchLink fieldCode="AU" term="%22Nakamura+Y%22">Nakamura Y</searchLink>; Department 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.<br /><searchLink fieldCode="AU" term="%22Yanagi+Y%22">Yanagi Y</searchLink>; Department 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.; Department of Oral and Maxillofacial Radiology, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.; Department of Oral and Maxillofacial Radiology, Division of Dentistry, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.; Preliminary Examination Room, Medical Development Field, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan.; Department 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.
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  Data: <searchLink fieldCode="JN" term="%228806621%22">Oral radiology</searchLink> [Oral Radiol] 2026 Oct; Vol. 42 (4), pp. 1184-1191. <i>Date of Electronic Publication: </i>2026 Aug 20.
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  Data: <searchLink fieldCode="MM" term="%22Radiology%22">Radiology*</searchLink>/<searchLink fieldCode="MM" term="%22Radiology+education%22">education</searchLink> <br /><searchLink fieldCode="MM" term="%22Generative+Artificial+Intelligence%22">Generative Artificial Intelligence*</searchLink> <br /><searchLink fieldCode="MM" term="%22Certification%22">Certification*</searchLink><br /><searchLink fieldCode="MH" term="%22Japan%22">Japan</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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).<br />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.<br />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.<br />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.<br /> (© 2026. The Author(s).)
– Name: Abstract
  Label: Competing Interests
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  Data: Declarations. Conflicts of interest: Takeshita Y, Kawazu T, Hisatomi M, Fujikura M, Okada S, Shibanuma A, Namba Y, Yoshida S, Yoshida S, Nakamura Y, Yanagi Y declare that they have no conflict of interest. Ethics approval: This article does not contain any studies with human or animal subjects performed by the any of the authors.
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  Label: References
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  Data: Nat Rev Cancer. 2018 Aug;18(8):500-510. (PMID: <searchLink fieldCode="PM" term="%2229777175%22">29777175)</searchLink><br />Radiology. 2023 Jul;308(1):e231040. (PMID: <searchLink fieldCode="PM" term="%2237462501%22">37462501)</searchLink><br />Dentomaxillofac Radiol. 2024 Sep 1;53(6):390-395. (PMID: <searchLink fieldCode="PM" term="%2238848473%22">38848473)</searchLink><br />Radiology. 2023 Jun;307(5):e230987. (PMID: <searchLink fieldCode="PM" term="%2237191491%22">37191491)</searchLink><br />Radiology. 2023 Nov;309(2):e232561. (PMID: <searchLink fieldCode="PM" term="%2237987662%22">37987662)</searchLink><br />Radiology. 2023 Apr;307(2):e230163. (PMID: <searchLink fieldCode="PM" term="%2236700838%22">36700838)</searchLink><br />Radiology. 2023 Jun;307(5):e231259. (PMID: <searchLink fieldCode="PM" term="%2237367439%22">37367439)</searchLink><br />BMC Med Educ. 2025 Oct 2;25(1):1319. (PMID: <searchLink fieldCode="PM" term="%2241039365%22">41039365)</searchLink><br />BMC Med Educ. 2025 Oct 10;25(1):1403. (PMID: <searchLink fieldCode="PM" term="%2241074130%22">41074130)</searchLink><br />J Dent Sci. 2024 Jul;19(3):1595-1600. (PMID: <searchLink fieldCode="PM" term="%2239035269%22">39035269)</searchLink><br />Healthcare (Basel). 2023 Mar 19;11(6):. (PMID: <searchLink fieldCode="PM" term="%2236981544%22">36981544)</searchLink><br />Radiology. 2023 Jun;307(5):e230582. (PMID: <searchLink fieldCode="PM" term="%2237191485%22">37191485)</searchLink><br />Radiology. 2024 Jan;310(1):e232411. (PMID: <searchLink fieldCode="PM" term="%2238226874%22">38226874)</searchLink><br />Radiology. 2023 May;307(4):e230424. (PMID: <searchLink fieldCode="PM" term="%2237014239%22">37014239)</searchLink><br />Multimed Tools Appl. 2023;82(3):3713-3744. (PMID: <searchLink fieldCode="PM" term="%2235855771%22">35855771)</searchLink><br />Jpn J Radiol. 2024 Feb;42(2):201-207. (PMID: <searchLink fieldCode="PM" term="%2237792149%22">37792149)</searchLink><br />BMC Oral Health. 2025 Jul 21;25(1):1229. (PMID: <searchLink fieldCode="PM" term="%2240691556%22">40691556)</searchLink><br />Dentomaxillofac Radiol. 2025 Nov 1;54(8):613-631. (PMID: <searchLink fieldCode="PM" term="%2240796316%22">40796316)</searchLink><br />Nat Med. 2023 Aug;29(8):1930-1940. (PMID: <searchLink fieldCode="PM" term="%2237460753%22">37460753)</searchLink><br />Yearb Med Inform. 2022 Aug;31(1):243-253. (PMID: <searchLink fieldCode="PM" term="%2235654422%22">35654422)</searchLink>
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