准备未来的医生:将数据科学纳入医学教育中
Rishi M Shah1, Kavya M Shah2, Piroz Bahar3
1Department of Applied Mathematics, Yale College, New Haven, CT USA.
医生需要在医疗保健中使用人工智能 (AI) 的数据科学 (DS) 技能. 将DS纳入医学教育对于未来的患者护理至关重要,尽管它目前不在课程中.
科学领域:
- 医疗教育 医学教育
- 数据科学数据科学数据科学
- 医疗保健中的人工智能
背景情况:
- 人工智能在医疗保健中的兴奋日益增长,凸显了医生采用新技术的必要性.
- 数据科学 (DS) 被医学学生和住院医生认为至关重要,但在医学院课程中基本上缺少.
- 未来的增强患者护理依赖于临床医生理解数据科学原则.
研究的目的:
- 评估目前医学院数据科学教育的现状.
- 用现实世界的例子来说明数据科学在医学上的影响.
- 探索DS整合的障碍,并为医学课程提出解决方案.
主要方法:
- 对医疗教育当前数据科学课程的审查.
- 分析AI在病理学分类和败血症检测中的应用.
- 探索DS整合的挑战和建议.
主要成果:
- 数据科学目前在医学院课程中代表性不足.
- 在病理学和败血症检测中的AI应用证明了DS的实际价值.
- 有几个障碍阻碍了将DS纳入医疗培训.
结论:
- 将数据科学整合到医学教育中对于为未来的医生准备人工智能驱动的医疗保健至关重要.
- 解决课程差距和克服实施挑战是必要的步骤.
- 需要积极的战略来为临床医生提供重要的数据科学技能.
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