Researchers worldwide build a decentralized learning model to improve COVID-19 diagnosis.

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Medical workers look over a chest x-ray of a patient suspected of having COVID-19.

A few months ago, Daniel L. Rubin, a professor of biomedical data science, of radiology, and of medicine at Stanford, received an unexpected request for collaboration. A group of researchers from China and Thailand were developing a new machine learning algorithm to improve the accuracy of radiology-based COVID-19 diagnosis and needed help to make their model more robust without compromising patient privacy. Rubin — whose research uses AI to extract biomedical information from radiology images to guide physicians — had the right tool for the challenge. …


Burcin Ikiz, Ph.D.

Neuroscientist. Science Writer & Communicator. Passionately Curious Human.

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