C.A. James is assistant professor, Departments of Internal Medicine and Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan.
K.M. Wheelock is an internal medicine house officer, Yale School of Medicine, New Haven, Connecticut.
Acad Med. 2021 Jul 1;96(7):954-957. doi: 10.1097/ACM.0000000000003943.
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.
机器学习 (ML) 算法是一种强大的预测工具,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,并且还有更多的工具正在开发中。医生是医疗保健系统中的重要利益相关者,但大多数医生没有能力就 ML 技术在患者护理中的部署和应用做出明智的决策。至关重要的是,将 ML 概念纳入医学课程,使医生能够成为新兴使用 ML 的工具的知情消费者。这种范式转变类似于 20 世纪 90 年代的循证医学 (EBM) 运动。当时,EBM 是一个新概念;现在,EBM 被认为是医学课程的一个重要组成部分,对提供高质量的患者护理至关重要。ML 有可能对医学实践产生类似的、甚至更大的影响。随着这项技术的不断推进,教育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。