In this issue of JAMA, Liu and colleagues1 provide a users’ guide to reading clinical machine learning articles. Beyond a synopsis of selected concepts in modern machine learning, the authors elaborate step-by-step guidance for physicians seeking to evaluate this evidence with a critical eye. In an era when readers are bombarded with artificial intelligence in everyday life, from credit card fraud warnings and smartphones that anticipate their needs to life-like videos of people who do not actually exist, the sanity check provided by this article is most welcome.
Doshi-Velez F, Perlis RH. Evaluating Machine Learning Articles. JAMA. 2019;322(18):1777–1779. doi:10.1001/jama.2019.17304
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