未来医生的人工智能的基础:一个实用,可访问的课程
Jonathan Theros1, Alan Soetikno1, David Liebovitz1
1Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Medical teacher
|February 13, 2025
概括
临床医生需要人工智能素养,用于医疗保健中的机器学习 (ML) 和大型语言模型 (LLM). 一个模块化的Colab课程提供了动手人工智能教育,促进了对这些变革性工具的关键参与.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
背景情况:
- 机器学习 (ML) 和大型语言模型 (LLM) 正在彻底改变医疗保健服务.
- 临床医生往往缺乏人工智能的基础知识,导致潜在的过度依赖和监督.
- 当前的医学课程在很大程度上忽略了实际的AI教育,造成了教育差距.
研究的目的:
- 为临床医生解决人工智能素养方面的教育差距.
- 提出一个实用,可访问的课程,用于教学基础AI概念.
- 促进临床实践中对人工智能工具的关键参与.
主要方法:
- 开发了一个模块化课程,利用谷歌协作 (Colab) 笔记本.
- 该课程涵盖数据科学,ML模型构建和LLM交互.
- 强调了适合非数据科学家的实践互动方法.
主要成果:
- 该课程由五个相互连接的模块设计,从基本的数据科学到高级的ML和LLMs.
- 科拉布的可访问,基于云的平台促进了人工智能应用的实践学习和探索.
- 模块化设计允许集成到现有的医学院数据科学线程中.
结论:
- 通过Colab笔记本提供的模块化实践课程可以有效地向临床医生教授基础AI概念.
- 这种方法促进了对人工智能工具的关键参与,这对于安全有效的医疗整合至关重要.
- 该课程可适应和扩展到各种教育环境,包括低资源环境.
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