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培养明日之医:医学教育中机器学习的理由

Preparing Tomorrow's Physicians: The Case for Machine Learning in Medical Education.

作者信息

Burwell Julian Michael

机构信息

Department of Medical Education, Geisinger Commonwealth School of Medicine, Scranton, PA, United States of America.

出版信息

J Med Syst. 2025 Jun 11;49(1):79. doi: 10.1007/s10916-025-02214-y.

Abstract

Machine learning should be integrated into medical curricula to prepare physicians-in-training for 21st-century practice conditions. This comment proposes practical implementation strategies that build upon existing educational frameworks by drawing parallels to traditional statistical methods. By incorporating these skills through a phased approach, medical education can fulfill its duty to the public by preparing future physicians to effectively evaluate emergent technology, identify potential sources of bias, and better serve patients.

摘要

机器学习应融入医学课程,以便让正在接受培训的医生为21世纪的执业环境做好准备。本评论提出了切实可行的实施策略,这些策略借鉴传统统计方法,以现有的教育框架为基础。通过分阶段的方式融入这些技能,医学教育可以履行其对公众的责任,培养未来的医生有效地评估新兴技术、识别潜在的偏差来源并更好地服务患者。

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