An MIT AI reporter considers the COVID-countering volunteerism of, among others, the founder of a machine-learning startup. He entered the fray when he saw that his parents weren’t getting it about the need to practice social distancing.

For finding microcalcifications in screening exams, full-field digital mammography now has little to recommend it over reduced-radiation 2D synthetic mammography.

The COVID-19 crisis is inspiring all manner of loudly touted innovations aimed at leveraging AI to beat back, stamp out or otherwise contain the pandemic. Some watchers aren’t convinced the efforts are worthy of the attention they’re getting.

Researchers from West Virginia University have launched a new study aimed at identifying healthcare workers infected with the new coronavirus before they become symptomatic.

As an aid to tumor-resection neurosurgery and its planning, fluorescence imaging has been limited by its tendency to scatter light. Researchers are working on technology that would overcome this shortcoming.

As the research mounts on AI matching or besting radiologists in discrete interpretive tasks, so does the excitement in the popular press. Now comes a study showing many of these studies only invite the hype.  

The trio’s platform is designed to help healthcare providers quickly find out where their locality stands on things like ventilator utilization, testing results ICU bed availability and COVID patient discharges.

In the face of the coronavirus crisis, all radiologists can do for some of their pathologist peers is stand, salute and express solidarity.