ChatGPT在台湾药师执照考试中的表现。

IF 1.9 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL Journal of the Chinese Medical Association Pub Date : 2023-07-01 Epub Date: 2023-07-05 DOI:10.1097/JCMA.0000000000000942
Ying-Mei Wang, Hung-Wei Shen, Tzeng-Ji Chen
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引用次数: 15

摘要

背景:ChatGPT是一种针对对话进行训练的人工智能模型。ChatGPT已广泛应用于普通医学教育和心脏病学,但在药学方面的应用还很欠缺。本研究旨在检验ChatGPT在台湾药师执业资格考试中的准确性,并探讨其在药学教育中的潜在作用。方法:采用ChatGPT对2023年台湾省第一次药师执业资格考试进行中英文对照。这些问题是人工一个接一个输入的。图表问题、化学式和表格被排除在外。文本题根据正确答案的数量进行评分。图表问题的得分是通过将文本问题的数量和正确率相乘来确定的。本研究于2023年3月5日至3月10日进行,使用ChatGPT 3.5。结果:ChatGPT汉语和英语答题正确率第一阶段分别为54.4%和56.9%,第二阶段分别为53.8%和67.6%。在语文测试中,只有药理学和药物化学部分获得了及格分数。各科英语成绩均高于汉语成绩,其中调剂药学、临床药学和治疗学成绩显著高于汉语成绩。结论:ChatGPT 3.5未通过台湾药师资格考试。虽然不能通过考试,但通过深度学习可以快速提高。它提醒我们,我们不仅应该用多项选择题来评估药剂师的能力,而且应该在未来使用更多种类的评估。药学教育应该随着考试的变化而改变,学生必须能够使用人工智能技术进行自学。更重要的是,我们需要帮助学生培养人文素质,加强与患者互动的能力,使他们成为有爱心的医护人员。
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Performance of ChatGPT on the pharmacist licensing examination in Taiwan.

Background: ChatGPT is an artificial intelligence model trained for conversations. ChatGPT has been widely applied in general medical education and cardiology, but its application in pharmacy has been lacking. This study examined the accuracy of ChatGPT on the Taiwanese Pharmacist Licensing Examination and investigated its potential role in pharmacy education.

Methods: ChatGPT was used on the first Taiwanese Pharmacist Licensing Examination in 2023 in Mandarin and English. The questions were entered manually one by one. Graphical questions, chemical formulae, and tables were excluded. Textual questions were scored according to the number of correct answers. Chart question scores were determined by multiplying the number and the correct rate of text questions. This study was conducted from March 5 to March 10, 2023, by using ChatGPT 3.5.

Results: The correct rate of ChatGPT in Chinese and English questions was 54.4% and 56.9% in the first stage, and 53.8% and 67.6% in the second stage. On the Chinese test, only pharmacology and pharmacochemistry sections received passing scores. The English test scores were higher than the Chinese test scores across all subjects and were significantly higher in dispensing pharmacy and clinical pharmacy as well as therapeutics.

Conclusion: ChatGPT 3.5 failed the Taiwanese Pharmacist Licensing Examination. Although it is not able to pass the examination, it can be improved quickly through deep learning. It reminds us that we should not only use multiple-choice questions to assess a pharmacist's ability, but also use more variety of evaluations in the future. Pharmacy education should be changed in line with the examination, and students must be able to use AI technology for self-learning. More importantly, we need to help students develop humanistic qualities and strengthen their ability to interact with patients, so that they can become warm-hearted healthcare professionals.

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来源期刊
Journal of the Chinese Medical Association
Journal of the Chinese Medical Association MEDICINE, GENERAL & INTERNAL-
CiteScore
6.20
自引率
13.30%
发文量
320
审稿时长
15.5 weeks
期刊介绍: Journal of the Chinese Medical Association, previously known as the Chinese Medical Journal (Taipei), has a long history of publishing scientific papers and has continuously made substantial contribution in the understanding and progress of a broad range of biomedical sciences. It is published monthly by Wolters Kluwer Health and indexed in Science Citation Index Expanded (SCIE), MEDLINE®, Index Medicus, EMBASE, CAB Abstracts, Sociedad Iberoamericana de Informacion Cientifica (SIIC) Data Bases, ScienceDirect, Scopus and Global Health. JCMA is the official and open access journal of the Chinese Medical Association, Taipei, Taiwan, Republic of China and is an international forum for scholarly reports in medicine, surgery, dentistry and basic research in biomedical science. As a vehicle of communication and education among physicians and scientists, the journal is open to the use of diverse methodological approaches. Reports of professional practice will need to demonstrate academic robustness and scientific rigor. Outstanding scholars are invited to give their update reviews on the perspectives of the evidence-based science in the related research field. Article types accepted include review articles, original articles, case reports, brief communications and letters to the editor
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