ChatGPT versus human in generating medical graduate exam multiple choice questions-A multinational prospective study (Hong Kong SAR, Singapore, Ireland, and the United Kingdom)
被引:46
作者:
Cheung, Billy Ho Hung
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Univ Hong Kong, LKS Fac Med, Hong Kong, Peoples R ChinaUniv Hong Kong, LKS Fac Med, Hong Kong, Peoples R China
Cheung, Billy Ho Hung
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Lau, Gary Kui Kai
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Univ Hong Kong, LKS Fac Med, Hong Kong, Peoples R ChinaUniv Hong Kong, LKS Fac Med, Hong Kong, Peoples R China
Lau, Gary Kui Kai
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Wong, Gordon Tin Chun
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Univ Hong Kong, LKS Fac Med, Hong Kong, Peoples R ChinaUniv Hong Kong, LKS Fac Med, Hong Kong, Peoples R China
Wong, Gordon Tin Chun
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Lee, Elaine Yuen Phin
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Univ Hong Kong, LKS Fac Med, Hong Kong, Peoples R ChinaUniv Hong Kong, LKS Fac Med, Hong Kong, Peoples R China
Lee, Elaine Yuen Phin
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Kulkarni, Dhananjay
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Univ Edinburgh, Dept Surg, Edinburgh, ScotlandUniv Hong Kong, LKS Fac Med, Hong Kong, Peoples R China
Kulkarni, Dhananjay
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Seow, Choon Sheong
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Natl Univ Canc Inst Singapore, Dept Surg, Singapore, SingaporeUniv Hong Kong, LKS Fac Med, Hong Kong, Peoples R China
Seow, Choon Sheong
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Wong, Ruby
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Univ Galway, Dept Surg, Galway, IrelandUniv Hong Kong, LKS Fac Med, Hong Kong, Peoples R China
Wong, Ruby
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Co, Michael Tiong-Hong
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Univ Hong Kong, LKS Fac Med, Hong Kong, Peoples R ChinaUniv Hong Kong, LKS Fac Med, Hong Kong, Peoples R China
Co, Michael Tiong-Hong
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机构:
[1] Univ Hong Kong, LKS Fac Med, Hong Kong, Peoples R China
IntroductionLarge language models, in particular ChatGPT, have showcased remarkable language processing capabilities. Given the substantial workload of university medical staff, this study aims to assess the quality of multiple-choice questions (MCQs) produced by ChatGPT for use in graduate medical examinations, compared to questions written by university professoriate staffs based on standard medical textbooks.Methods50 MCQs were generated by ChatGPT with reference to two standard undergraduate medical textbooks (Harrison's, and Bailey & Love's). Another 50 MCQs were drafted by two university professoriate staff using the same medical textbooks. All 100 MCQ were individually numbered, randomized and sent to five independent international assessors for MCQ quality assessment using a standardized assessment score on five assessment domains, namely, appropriateness of the question, clarity and specificity, relevance, discriminative power of alternatives, and suitability for medical graduate examination.ResultsThe total time required for ChatGPT to create the 50 questions was 20 minutes 25 seconds, while it took two human examiners a total of 211 minutes 33 seconds to draft the 50 questions. When a comparison of the mean score was made between the questions constructed by A.I. with those drafted by humans, only in the relevance domain that the A.I. was inferior to humans (A.I.: 7.56 +/- 0.94 vs human: 7.88 +/- 0.52; p = 0.04). There was no significant difference in question quality between questions drafted by A.I. versus humans, in the total assessment score as well as in other domains. Questions generated by A.I. yielded a wider range of scores, while those created by humans were consistent and within a narrower range.ConclusionChatGPT has the potential to generate comparable-quality MCQs for medical graduate examinations within a significantly shorter time.