Machine learning’s expansive capacity to quickly turn big health data into evidence-based care will challenge all practitioners of medicine to either grow along with the technology or accept getting left behind by it. And radiologists will be among the first to feel its push (if they’re not among the rads who are already working with it). 

Four breast radiologists are calling for the creation of a national imaging repository housed in the cloud and spearheaded by their specialty. 

Certain kinds of cancer, including prostate cancer, are famously hard to detect using PET scans, the method for which most kinds of cancerous tumors are located in the body. But another type of nuclear imaging could help detect tumors regular PET scans can’t see, according to the Wall Street Journal. 

Want to read up on how artificial intelligence and its technological kin are soon to re-shape healthcare, including imaging, as we’ve known it? Look anywhere. You’ll find yet another article on the topic.

In developing algorithms to differentiate between suspicious nodules in the thyroid gland, researchers in China have found that their machine-learning computations separate malignant from benign properties more accurately than an inexperienced radiologist—but not as accurately as the experienced radiologist whose know-how was used to create the algorithms.

Remember the MRI study from Hungary showing that dogs seem to understand not only humans’ tone but also our words? The Washington Post has asked the burning follow-up question a lot of dog lovers have surely been asking: How the heck did they get awake dogs to lie still in a scanner for eight minutes? 

Imaging departments were not immune as all data systems were taken down at Appalachian Regional Healthcare, which serves parts of Kentucky and West Virginia, after an apparent cyberattack on Aug. 29. 

Enterprise imaging strategies are priorities at many provider institutions. However, interoperability issues are standing between wish lists and project assignments—and IT leaders in provider organizations are wary about the potential for “unattainable image data” to set back patient care.