Academic experts in disaster response have developed an AI-based simulation model that accurately predicts ebbs and flows in traffic at drive-through vaccination stations.

A few months into the COVID crisis, the health department of California’s Contra Costa County faced an unexpected side challenge: Staff were getting inundated by faxes bearing vital health data.

Oxford researchers have developed and prospectively validated two AI tools that can quickly screen hospital patients for COVID-19 using routine clinical data.

Researchers have demonstrated a machine learning model that can rule out COVID-19 in many emergency patients—and all the algorithm needs is data from routine ER blood tests.

A healthcare startup whose AI-powered symptom checker lets users check symptoms against other patients’ experiences has begun a partnership with Mayo Clinic and raised a fresh $42 million to advance its telehealth platform.  

Nonprofit competition organizer XPrize is partnering with global tech consultancy Cognizant to award AI innovators in a pandemic response challenge.

The COVID crisis has given AI a chance to shine for medical researchers this year—and shine it has, judging by one prominent center’s experience.

Almost half the 540-participant cohort is already using at least one form of AI.

The novel coronavirus has shown a nasty penchant for targeting the kidneys, and physicians can’t always tell which patients will need dialysis until they do. By then it’s often too late to save a life.

Knowing that antibodies as well as viruses bind to certain proteins in certain ways, scientists have come up with a technique to watch both sickness-signaling materials stick to microscopic beads prepared for the purpose.

The COVID-19 pandemic is a bad dream in the truest sense of the word—and AI helps to prove it, a team of researchers assert in a study published online Oct. 1 in Frontiers in Psychology.