Preiksaitis Carl, Rose Christian
Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.
JMIR Med Educ. 2023 Oct 20;9:e48785. doi: 10.2196/48785.
Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT and Bard, can generate new content and have a wide range of possible applications.
This study aimed to synthesize the potential opportunities and limitations of generative AI in medical education. It sought to identify prevalent themes within recent literature regarding potential applications and challenges of generative AI in medical education and use these to guide future areas for exploration.
We conducted a scoping review, following the framework by Arksey and O'Malley, of English language articles published from 2022 onward that discussed generative AI in the context of medical education. A literature search was performed using PubMed, Web of Science, and Google Scholar databases. We screened articles for inclusion, extracted data from relevant studies, and completed a quantitative and qualitative synthesis of the data.
Thematic analysis revealed diverse potential applications for generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlighted significant challenges, such as issues with academic integrity, data accuracy, and potential detriments to learning. Based on these themes and the current state of the literature, we propose the following 3 key areas for investigation: developing learners' skills to evaluate AI critically, rethinking assessment methodology, and studying human-AI interactions.
The integration of generative AI in medical education presents exciting opportunities, alongside considerable challenges. There is a need to develop new skills and competencies related to AI as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.
生成式人工智能（AI）技术在各个领域的应用日益广泛，人们对其在医学教育中的潜在应用既充满兴趣又有所担忧。这些技术，如Chat GPT和Bard，能够生成新内容，具有广泛的潜在应用。
本研究旨在综合生成式AI在医学教育中的潜在机遇和局限性。它试图识别近期文献中关于生成式AI在医学教育中的潜在应用和挑战的普遍主题，并以此指导未来的探索领域。
我们按照Arksey和O'Malley的框架进行了一项范围综述，纳入了2022年以后发表的、在医学教育背景下讨论生成式AI的英文文章。使用PubMed、科学网和谷歌学术数据库进行文献检索。我们筛选文章以确定其是否纳入，从相关研究中提取数据，并对数据进行定量和定性综合分析。
主题分析揭示了生成式AI在医学教育中的多种潜在应用，包括自主学习、模拟场景和写作辅助。然而，文献也强调了重大挑战，如学术诚信问题、数据准确性以及对学习的潜在危害。基于这些主题和文献现状，我们提出以下3个关键研究领域：培养学习者批判性评估AI的技能、重新思考评估方法以及研究人机交互。
将生成式AI整合到医学教育中既带来了令人兴奋的机遇，也带来了巨大挑战。有必要培养与AI相关的新技能和能力，以及深思熟虑、细致入微的方法，以审视生成式AI在医学教育中日益增加的应用。
