Abstract: A method is provided for processing medical text and associated medical images. A natural language processor configured as a deep conventional neural network is trained on a first corpus of curated free-text, medical reports each of which having one or more structured labels assigned by an medical expert. The network is trained to learn to read additional free-text medical reports and produce predicted structured labels. The natural language processor is applied to a second corpus of free-text medical reports that are associated with medical images. The natural language processor generates structured labels for the associated medical images. A computer vision model is trained using the medical images and the structured labels generated. The computer vision model can thereafter assign a structured label to a further input medical image. In one example, the medical images are chest X-rays.

Abstract: A method is provided for processing medical text and associated medical images. A natural language processor configured as a deep conventional neural network is trained on a first corpus of curated free-text, medical reports each of which having one or more structured labels assigned by an medical expert. The network is trained to learn to read additional free-text medical reports and produce predicted structured labels. The natural language processor is applied to a second corpus of free-text medical reports that are associated with medical images. The natural language processor generates structured labels for the associated medical images. A computer vision model is trained using the medical images and the structured labels generated. The computer vision model can thereafter assign a structured label to a further input medical image. In one example, the medical images are chest X-rays.

Abstract: A wireless device, an app on a wireless device, and a method for automated diagnosis of radiographs is described. The app prompts a user to capture a photograph of a radiograph external to the mobile device with the mobile device's camera. The quality of the photograph is assessed and an error condition is reported if the quality is insufficient. A module displays on the mobile device display (1) a diagnosis that is assigned to the radiographs and (2) at least one similar radiograph. The diagnosis is assigned by subjecting the photograph to a deep learning model trained on a large corpus of labelled radiographs. The deep learning model can be resident on the mobile device or in a back end server. The app includes tools for enabling the user to select and navigate the input photograph and the similar radiograph by means of hand gestures on the display, and a tool for displaying medical knowledge associated with the diagnosis.