Developing an App to interpret Chest X-rays to support the diagnosis of respiratory pathology with Artificial Intelligence
This work addresses diagnostic challenges for patients in underserved regions, but it appears incremental as it applies existing AI methods to a new deployment context.
The researchers tackled the problem of limited access to medical diagnosis in remote areas by developing a smartphone app that uses an artificial neural network to interpret chest X-rays for early detection of life-threatening respiratory conditions, though no concrete performance numbers are provided.
In this paper we present our work to improve access to diagnosis in remote areas where good quality medical services may be lacking. We develop new Machine Learning methodologies for deployment onto mobile devices to help the early diagnosis of a number of life-threatening conditions using X-ray images. By using the latest developments in fast and portable Artificial Intelligence environments, we develop a smartphone app using an Artificial Neural Network to assist physicians in their diagnostic.