Artificial Intelligence for Anesthesia

Michael R. Mathis, Sachin Kheterpal, Kayvan Najarian
<span title="">2018</span> <i title="Ovid Technologies (Wolters Kluwer Health)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cjmzhtkkdjgjphz5zuzahenz2a" style="color: black;">Anesthesiology</a> </i> &nbsp;
Editorial Machine learning, the quintessential tool currently driving forward the development of artificial intelligence, was discovered and developed decades ago. Nevertheless, only recently has machine learning seen an exponential increase in growth, sophistication, and influence. Recent success stories outside healthcare are numerous, including: in 2014 Facebook unveiled DeepFace, a machine learning technology capable of identifying faces with 97.25% accuracy (compared to human accuracy of
more &raquo; ... .53%). 1 In 2016 Google adopted a deep learning approach to language translation, using an algorithm which is fed massive amounts of data to effectively train itself to recognize patterns in speech, with a reduction in translation errors by 87%. 2
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