By: Catherine Graham

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Wooden blocks form the shape of the human brain in front of a teal background.

With a little help from AI

In a new course offered by computer scientist Mathias Unberath, engineering students design AI systems that integrate seamlessly into human lives.

A graphic representing the Johns Hopkins solution to imaging scan problems from metal implants. The scanner can be steered (ball on right encircled by green and yellow lines) to get an undistorted image of the implant. Greek letters stand for the best way to steer the scanner to achieve this.

Johns Hopkins researchers solve imaging scan problems caused by metal implants

John C. Malone Professor Jeffrey Siewerdsen and team solve problem of distorted imaging scans that plague surgeons who need to use them to assess the placement of metal implants.

Red, gray, and yellow coronavirus molecules.

New research confirms higher rates of new coronavirus in Latinx populations

A study of testing results across Johns Hopkins Medicine testing sites highlights coronavirus racial disparities in the Baltimore-Washington area.

A mobile testing booth in a parking lot.

Mobile COVID-19 testing booths keep healthcare providers safe, CBS Baltimore

Researchers from University of Maryland, Johns Hopkins University, and the Robert Fischell Institute for Biomedical Devices have developed a mobile testing booth that will give healthcare providers the ability to test patients for COVID-19 without risking their own health.

Blue lines connected by dots in front of an orange lens flare.

Artificial Intelligence-based Clinical Decision Support for COVID-19 – Where Art Thou?

Malone faculty discuss the notable absence of AI-based clinical decision support (CDS) in the early phases of the COVID-19 pandemic, and identify opportunities to improve "AI readiness" for future healthcare challenges.

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.