In: COVID-19

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Two X-rays of the lungs, one in grayscale and one overlaid with rainbow colors centered on the left lung.

Radiologists use deep learning to find signs of COVID-19 in chest x-rays

Johns Hopkins radiologists have found that a deep learning algorithm to detect tuberculosis (TB) in chest X-rays could be useful for identifying lung abnormalities related to COVID-19.

An illustration of a mask made of lines and dots acting as a shield from smaller dots.

The ill winds of COVID-19

Johns Hopkins mechanical engineers believe fluid dynamics can tell us a great deal about the COVID-19 pandemic—and how people can protect themselves when the country reopens.

A pill bottle and white pills. The bottle is titled Hydroxychloroquine 200 MG tab.

Public demand for unproven COVID-19 therapies rise after endorsements from high-profile figures

A new study by researchers from Johns Hopkins, the University of Oxford, and the University of California, San Diego, examines Americans’ Google searches to track the rising public demand for these unproven drugs soon after these high-profile endorsements.

A green and black globe connected by coronavirus molecules.

New outbreak model better predicts COVID-19 hotspots

Team led by Malone researchers is developing a new model that more accurately understands and predicts the spread of diseases such as COVID-19 in both large and small communities.

Doctors gather around a patient's bed in the background. In the foreground, a monitor displaying the patient's vital signs.

AI Can Help Hospitals Triage COVID-19 Patients, CS’s Suchi Saria, IEEE Spectrum

As the coronavirus pandemic brings floods of people to hospital emergency rooms around the world, physicians are struggling to triage patients,...

A robot tends to a COVID-19 patient in the intensive care unit of an Italian hospital.

Meet humanity’s new ally in the coronavirus fight: Robots, CS’s Russ Taylor, Los Angeles Times

John C. Malone Professor Russell Taylor tells The L.A. Times that medical robots could be useful in intensive care units where risk of contamination is a major worry.