大型语言模型在医学教育中的作用:应用和含义
Conrad W Safranek1, Anne Elizabeth Sidamon-Eristoff2, Aidan Gilson1
1Section for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States.
JMIR medical education
|August 14, 2023
概括
大型语言模型 (LLM) 为医学学生提供了好处,有助于差异诊断和实践案例. 然而,LLM在教学关键的患者互动和上下文理解方面存在局限性.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 数字健康数字健康
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 在医学教育中越来越多地被讨论.
- 对于将它们纳入医疗保健,人们既热衷又担心.
- 医学学生正在积极探索这些学习工具.
研究的目的:
- 从学生的角度评估LLMs,特别是ChatGPT在医学教育中的实用性.
- 确定在临床前医学培训中LLM的积极用例和局限性.
- 为学生和教育工作者提供有关在医疗保健中导航人工智能的信息.
主要方法:
- 来自医学学生与ChatGPT的沉浸式互动的定性见解.
- 与现有关于医疗保健LLM的研究进行上下文化.
- 识别和分析特定的用例和限制.
主要成果:
- 积极的用例包括差异诊断头脑风暴,互动案例实践和多选择题审查.
- 法律学士可以支持基础知识和可信任的专业活动学习.
- 确定的局限性包括难以教导上下文信息,非语言线索,建立关系和与更广泛的目标保持一致.
结论:
- 医学学生可以利用LLM来增强学习并了解AI的能力和缺点.
- 这种理解对于适应人工智能集成的医疗保健未来至关重要.
- 作为补充工具,LLM有希望,但不能取代医疗培训和实践的基本人类方面.
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