Are computers better than doctors ? – Judy Gichoya – Medium

For the month of January, we addressed the performance of deep learning algorithms for disease diagnosis, specifically focusing on the paper by the stanford group -- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. We continue to generate a large interest in the journal club, with 347 people registered, 150 of whom signed on January 24th 2018 to participate in the discussion. The paper has had 3 revisions and is available here https://arxiv.org/abs/1711.05225 . Like many deep learning papers that claim super human performance, the paper was widely circulated in the news media, several blog posts, on reddit and twitter. Please note that the findings of superhuman performance are increasingly being reported in medical AI papers. For example, this article denotes that "Medical AI May Be Better at Spotting Eye Disease Than Real Doctors" To help critique the ChexNet paper, we constituted a panel composed of the author team (most of the authors listed on the paper were kind enough to be in attendance -- thank you!), Dr. Luke(blog) and Dr. Paras (blog) who had critiqued the data used and Jeremy Howard (past president and chief scientist of Kaggle, a data analytics competition site, Ex-CEO of Enlitic, a healthcare imaging company, and the Current CEO of Fast.ai, a deep learning educational site) to provide insight to deep learning methodology.