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对大型语言模型的系统审查及其在医学教育中的含义

Harrison C Lucas¹, Jeffrey S Upperman², Jamie R Robinson^2,3

¹Brandeis University, Waltham, Massachusetts, USA.

Medical education

|April 19, 2024

概括

大型语言模型 (LLM) 通过提供个性化的学习和解决信息过载,显示了医学教育的潜力. 负责任的整合需要关注准确性,技能培养和伦理考虑,以获得最佳的学生学习体验.

科学领域:

医疗教育技术技术医学教育
医疗保健中的人工智能
数字学习工具数字学习工具

背景情况:

大型语言模型 (LLM) 正在成为变革性工具,在包括医学教育在内的各个领域具有重大潜力.
医学学生面临诸如信息过载和时间限制等挑战,需要创新的教育解决方案.
法学士在医学教育中的整合,为教育工作者和学生提供了增强学习的机会和关键挑战.

研究的目的:

系统地审查大型语言模型 (LLM) 在医学教育中的应用.
探索LLMs对医学学生学习经验的影响.
确定与医学培训中LLM使用相关的关键主题,好处和挑战.

主要方法:

从ChatGPT的首次亮相到2024年2月,在PubMed,Web of Science和Embase进行了系统的文献搜索.
使用与LLM和医学教育相关的关键词,重点是全文,英语文章.
两位独立审查员批评评价了所选研究的可信度.

主要成果:

在166项确定的研究中,有40项是相关的,突出了法学士能力,个性化学习的好处和内容准确性的挑战.
很大一部分研究 (42.5%) 在医学检查和临床信息环境中评估了像ChatGPT这样的LLM,证明了人类水平的性能潜力.
其余的研究广泛讨论了LLMs的未来作用,表明尽管目前的局限性,但对未来的应用有很强的兴趣.

结论:

在医学教育中负责任地实施LLM可以增强学生的学习体验.
确保信息准确性,优先考虑技能发展,并保持道德保障对于有效的LLM整合至关重要.
持续的批判性评估和跨学科的合作对于成功地在医学培训中采用LLMs至关重要.

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