利用数据马拉松在本科医学教育中教授AI:案例研究
Michael Steven Yao1,2,3, Lawrence Huang3,4, Emily Leventhal3,5
1Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
JMIR medical education
|April 16, 2025
概括
医学实习生通过实习生领导的数据马拉松提高了他们的数据科学和机器学习技能. 这些活动证明了学习人工智能在患者护理中的应用的有效性.
科学领域:
- 医学教育 医学教育
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在临床实践中越来越重要.
- 未来的医生需要了解AI/ML对患者护理的影响.
研究的目的:
- 描述由实习生领导的数据马拉松作为教学数据科学和机器学习技能的有效方法.
- 提供有关数据马拉松实施的见解和未来举措的经验教训.
主要方法:
- 详细介绍MDplus组织的两个多机构数据马拉松.
- 通过选择参与,非识别的数据马拉松后调查来评估有效性.
- 分析了调查答复,以评估参与者的经验,并确定需要改进的领域.
主要成果:
- 大约有200名医疗学员参加了数字数据马拉松 (2023-2024年).
- 参与者表现出了改善的Python技能,用于医疗数据分析.
- 调查受访者报告说,他们感到愉快,并提高了从数据中获得临床见解的能力.
结论:
- 数据马拉松是医疗学员的有效,经济高效的教育工具.
- 数据马拉松提高了学员从数据中产生临床上有意义的见解的能力.
- 数据马拉松提高了数据科学和人工智能的技能,以改善患者护理.
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