  The chest X-ray (CXR) is by far the most commonly performed radiological
examination for screening and diagnosis of many cardiac and pulmonary diseases.
There is an immense world-wide shortage of physicians capable of providing
rapid and accurate interpretation of this study. A radiologist-driven analysis
of over two million CXR reports generated an ontology including the 40 most
prevalent pathologies on CXR. By manually tagging a relatively small set of
sentences, we were able to construct a training set of 959k studies. A deep
learning model was trained to predict the findings given the patient frontal
and lateral scans. For 12 of the findings we compare the model performance
against a team of radiologists and show that in most cases the radiologists
agree on average more with the algorithm than with each other.
