Li Zhui, Nina Yhap, Liu Liping, Wang Zhengjie, Xiong Zhonghao, Yuan Xiaoshu, Cui Hong, Liu Xuexiu, Ren Wei
{"title":"Impact of Large Language Models on Medical Education and Teaching Adaptations","authors":"Li Zhui, Nina Yhap, Liu Liping, Wang Zhengjie, Xiong Zhonghao, Yuan Xiaoshu, Cui Hong, Liu Xuexiu, Ren Wei","doi":"10.2196/55933","DOIUrl":null,"url":null,"abstract":"This viewpoint article explores the transformative impact of Chat Generative Pre-trained Transformer (ChatGPT) on medical education, highlighting its opportunities and challenges. ChatGPT, a product of OpenAI, leverages advanced deep learning models to offer diverse applications, including enhancing teaching efficiency, facilitating personalized learning, reinforcing clinical skills training, improving medical teaching assessment, enhancing efficiency in medical research, and supporting continuing medical education. While presenting promising opportunities, the integration of ChatGPT in medical education raises concerns about response accuracy, overreliance, lack of emotional intelligence, and privacy and data security risks. The article underscores the imperative need to carefully address these challenges, outlining future pathways to bolster medical information accuracy, fortify privacy and data security, and promote synergy between ChatGPT and other artificial intelligence technologies in medical education. It highlights the adaptability and transformative significance of educators amid the widespread integration of ChatGPT in medical education. Educators must consistently uphold a leadership role, guiding students in the ethical and effective use of ChatGPT, nurturing independent thinking, and honing critical reasoning skills. Safeguarding the quality and integrity of medical education in this dynamic technological era remains paramount.","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"66 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/55933","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
This viewpoint article explores the transformative impact of Chat Generative Pre-trained Transformer (ChatGPT) on medical education, highlighting its opportunities and challenges. ChatGPT, a product of OpenAI, leverages advanced deep learning models to offer diverse applications, including enhancing teaching efficiency, facilitating personalized learning, reinforcing clinical skills training, improving medical teaching assessment, enhancing efficiency in medical research, and supporting continuing medical education. While presenting promising opportunities, the integration of ChatGPT in medical education raises concerns about response accuracy, overreliance, lack of emotional intelligence, and privacy and data security risks. The article underscores the imperative need to carefully address these challenges, outlining future pathways to bolster medical information accuracy, fortify privacy and data security, and promote synergy between ChatGPT and other artificial intelligence technologies in medical education. It highlights the adaptability and transformative significance of educators amid the widespread integration of ChatGPT in medical education. Educators must consistently uphold a leadership role, guiding students in the ethical and effective use of ChatGPT, nurturing independent thinking, and honing critical reasoning skills. Safeguarding the quality and integrity of medical education in this dynamic technological era remains paramount.
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.