Machine Learning: The Next Paradigm Shift in Medical Education
Machine Learning: The Next Paradigm Shift in Medical Education
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DOI：
10.1097/acm.0000000000003943
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发表时间：
2021-07-01
影响因子：
7.4
通讯作者：
Woolliscroft, James O.
中科院分区：
文献类型：
--
作者：
James, Cornelius A.;Wheelock, Kevin M.;Woolliscroft, James O.
机器学习（ML）算法是强大的预测工具，在临床环境中具有巨大的潜力。有许多现有的临床工具使用ML，还有更多正在开发中。医生是医疗保健系统中重要的利益相关者，但大多数医生都没有能力就ML技术在患者护理中的部署和应用做出明智的决定。将机器学习概念整合到医学课程中，使医生成为使用机器学习的新兴工具的知情消费者，这一点至关重要。这种范式转变类似于20世纪90年代的循证医学（EBM）运动。在当时，循证医学是一个新颖的概念；现在，循证医学被认为是医学课程的重要组成部分，对提供高质量的病人护理至关重要。ML有可能对医学实践产生类似的影响，如果不是更大的话。随着这项技术继续不可阻挡地向前发展，教育工作者必须继续评估医学课程，以确保医生接受培训，成为未来医疗保健的知情利益相关者。
Machine learning (ML) algorithms are powerful prediction tools with immense potential in the clinical setting. There are a number of existing clinical tools that use ML, and many more are in development. Physicians are important stakeholders in the health care system, but most are not equipped to make informed decisions regarding deployment and application of ML technologies in patient care. It is of paramount importance that ML concepts are integrated into medical curricula to position physicians to become informed consumers of the emerging tools employing ML. This paradigm shift is similar to the evidence-based medicine (EBM) movement of the 1990s. At that time, EBM was a novel concept; now, EBM is considered an essential component of medical curricula and critical to the provision of high-quality patient care. ML has the potential to have a similar, if not greater, impact on the practice of medicine. As this technology continues its inexorable march forward, educators must continue to evaluate medical curricula to ensure that physicians are trained to be informed stakeholders in the health care of tomorrow.