Application of ChatGPT in multilingual medical education: How does ChatGPT fare in 2023's Iranian residency entrance examination

Background: ChatGPT is a large language model (LLM) artificial intelligence instrument trained on massive amounts of text data extracted from the internet and/or user input. In the present article, we aim to apply the latest version of ChatGPT to the Iranian Medical Residency Examination. Methods: T...

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Bibliographic Details
Main Authors: Hamid Khorshidi, Afshin Mohammadi, David M. Yousem, Jamileh Abolghasemi, Golnoosh Ansari, Mohammad Mirza-Aghazadeh-Attari, U Rajendra Acharya, Ali Abbasian Ardakani
Format: Article
Language:English
Published: Elsevier 2023-01-01
Series:Informatics in Medicine Unlocked
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Online Access:http://www.sciencedirect.com/science/article/pii/S2352914823001600
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Summary:Background: ChatGPT is a large language model (LLM) artificial intelligence instrument trained on massive amounts of text data extracted from the internet and/or user input. In the present article, we aim to apply the latest version of ChatGPT to the Iranian Medical Residency Examination. Methods: The Iranian Medical Residency Examination is composed of 200 multichoice questions covering all domains of medicine. We used ChatGPT to translate questions into English, French, and Spanish. We fed the questions as multiple-choice questions and allowed ChatGPT to provide comprehensive answers and justifications for its choices. Results: ChatGPT was able to answer 161 (81.3% = 161/198) questions correctly when the Persian language was used. When the questions were translated into English, French, and Spanish, ChatGPT answered six, one, and five additional questions correctly, respectively. When comparing the different languages, there was no significant difference in the functioning of ChatGPT in different languages using either the McNemar test or the Binomial test. Conclusion: ChatGPT can deliver above-average performance in the Iranian Medical Residency Examination, demonstrating its potential for using language models in medicine.
ISSN:2352-9148