A deep-learning algorithm has accurately measured 26 pairs of uneven leg lengths on children’s x-rays at a rate 96 times faster than that recorded by an experienced, subspecialty-trained pediatric radiologist using manual means.

Unlearn.AI, a San Francisco-based AI technology company, has raised $12 million in Series A financing.

The feat may offer a way for physicians to watch for signs of deterioration in patients who have too mild a case of the disease to warrant hospitalization but may need quick response if the condition worsens.

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.