'Chester' is a healthcare web tool to diagnose chest X-rays using neural networks. All processing is done on your device and images are not sent to the server.
The official white paper is here with the following summary -
Deep learning has shown promise to augment radiologists and improve the standard of care globally. Two main issues that complicate deploying these systems are patient privacy and scaling to the global population. To deploy a system at scale with minimal computational cost while preserving privacy we present a web delivered (but locally run) system for diagnosing chest X-Rays. Code is delivered via a URL to a web browser (including cell phones) but the patient data remains on the users machine and all processing occurs locally. The system is designed to be used as a reference where a user can process an image to confirm or aid in their diagnosis.
The system contains three main components: out-of-distribution detection, disease prediction, and prediction explanation. The system is completely open source and freely available here
This app was built by Joseph Paul Cohen, Paul Bertin, and Vincent Frappier at The Montreal Institute for Learning Algorithms
If you have a chest X-ray you can upload it here for instant results. It even works on mobile devices.
NB - the model does take a while to download as it is quite large, plus you may need to disable your ad-blocker for it to work.