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

TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays