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ChatGPT 在医学教育中的应用现状:潜力、挑战与策略。

Current Status of ChatGPT Use in Medical Education: Potentials, Challenges, and Strategies.

机构信息

Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China.

Xiangya School of Nursing, Central South University, Changsha, China.

出版信息

J Med Internet Res. 2024 Aug 28;26:e57896. doi: 10.2196/57896.

DOI:10.2196/57896
PMID:39196640
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11391159/
Abstract

ChatGPT, a generative pretrained transformer, has garnered global attention and sparked discussions since its introduction on November 30, 2022. However, it has generated controversy within the realms of medical education and scientific research. This paper examines the potential applications, limitations, and strategies for using ChatGPT. ChatGPT offers personalized learning support to medical students through its robust natural language generation capabilities, enabling it to furnish answers. Moreover, it has demonstrated significant use in simulating clinical scenarios, facilitating teaching and learning processes, and revitalizing medical education. Nonetheless, numerous challenges accompany these advancements. In the context of education, it is of paramount importance to prevent excessive reliance on ChatGPT and combat academic plagiarism. Likewise, in the field of medicine, it is vital to guarantee the timeliness, accuracy, and reliability of content generated by ChatGPT. Concurrently, ethical challenges and concerns regarding information security arise. In light of these challenges, this paper proposes targeted strategies for addressing them. First, the risk of overreliance on ChatGPT and academic plagiarism must be mitigated through ideological education, fostering comprehensive competencies, and implementing diverse evaluation criteria. The integration of contemporary pedagogical methodologies in conjunction with the use of ChatGPT serves to enhance the overall quality of medical education. To enhance the professionalism and reliability of the generated content, it is recommended to implement measures to optimize ChatGPT's training data professionally and enhance the transparency of the generation process. This ensures that the generated content is aligned with the most recent standards of medical practice. Moreover, the enhancement of value alignment and the establishment of pertinent legislation or codes of practice address ethical concerns, including those pertaining to algorithmic discrimination, the allocation of medical responsibility, privacy, and security. In conclusion, while ChatGPT presents significant potential in medical education, it also encounters various challenges. Through comprehensive research and the implementation of suitable strategies, it is anticipated that ChatGPT's positive impact on medical education will be harnessed, laying the groundwork for advancing the discipline and fostering the development of high-caliber medical professionals.

摘要

ChatGPT 是一种基于生成的预训练转换器,自 2022 年 11 月 30 日推出以来,引起了全球的关注,并引发了讨论。然而,它在医学教育和科学研究领域引起了争议。本文探讨了 ChatGPT 的潜在应用、局限性和使用策略。

ChatGPT 通过其强大的自然语言生成能力为医学生提供个性化的学习支持,能够提供答案。此外,它在模拟临床场景、促进教学和学习过程以及振兴医学教育方面具有显著的应用。然而,这些进步也伴随着许多挑战。在教育方面,重要的是要防止过度依赖 ChatGPT 和打击学术剽窃。同样,在医学领域,必须确保 ChatGPT 生成的内容的及时性、准确性和可靠性。同时,还存在伦理挑战和信息安全问题。

针对这些挑战,本文提出了有针对性的策略。首先,必须通过思想教育、培养综合能力和实施多样化的评价标准来减轻对 ChatGPT 的过度依赖和学术剽窃的风险。将现代教学方法与 ChatGPT 的使用相结合,可提高医学教育的整体质量。为了提高生成内容的专业性和可靠性,建议采取措施,对 ChatGPT 的训练数据进行专业优化,并提高生成过程的透明度。这确保了生成的内容符合最新的医疗实践标准。此外,通过增强价值一致性和制定相关立法或实践准则,可以解决包括算法歧视、医疗责任分配、隐私和安全在内的伦理问题。

总之,虽然 ChatGPT 在医学教育中具有巨大的潜力,但也面临着各种挑战。通过全面的研究和实施适当的策略,有望利用 ChatGPT 对医学教育的积极影响,为推进医学教育学科发展和培养高素质医学专业人才奠定基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f0/11391159/c984169e57ca/jmir_v26i1e57896_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f0/11391159/5a822d0fa2b5/jmir_v26i1e57896_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f0/11391159/e225d5b2ef6a/jmir_v26i1e57896_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f0/11391159/9595ca42c8c5/jmir_v26i1e57896_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f0/11391159/c984169e57ca/jmir_v26i1e57896_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f0/11391159/5a822d0fa2b5/jmir_v26i1e57896_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f0/11391159/e225d5b2ef6a/jmir_v26i1e57896_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f0/11391159/9595ca42c8c5/jmir_v26i1e57896_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f0/11391159/c984169e57ca/jmir_v26i1e57896_fig4.jpg

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