From 2017 to 2018 the number of scientific publications found via PubMed search using the keyword “Machine Learning” increased by 46% (4,317 to 6,307). The results of studies involving machine learning, artificial intelligence (AI), and big data have captured the attention of healthcare practitioners, healthcare managers, and the public at a time when Western medicine grapples with unmitigated cost increases and public demands for accountability. The complexity involved in healthcare applications of machine learning and the size of the associated data sets has afforded many researchers an uncontested opportunity to satisfy these demands with relatively little oversight. While there is great potential for machine learning algorithms to positively impact healthcare related costs and outcomes, its deployment cannot be regarded in the same way a shipping optimization algorithm is considered at an Amazon warehouse. Those involved in the care of the infirm appreciate the incredibly high expectation for performance and more importantly the unique obligation to primum non nocere (first do no harm).