Nearly a year after it was first announced the Department of Veterans Affairs would be replacing its in-house electronic health record (EHR) system with a Cerner platform, the contract hasn’t been finalized. A senior VA official now says the agency’s acting chief has set a deadline of Memorial Day—May 28—to make a decision on the acquisition.

After reporting nearly $18 million in savings for the employers current participating in its bundled payment programs for spinal surgeries and joint replacements, UnitedHealth Group announced it will expand the program to nine additional markets.

Researchers from Brigham and Women's Hospital in Boston have created a tool capable of predicting if patients with mycosis fungoides (MF) will develop a deadly form of the cancer. Findings were published May 9 in Science Translational Medicine.

Researchers from the University of Texas MD Anderson Cancer Center in Houston have developed artificial intelligence (AI) with the aim to improve the accuracy of patient-specific clinical target volumes in cancer patients. Findings were published in the June 2018 issue of the International Journal of Radiation Oncology.

Building the infrastructure to support the accelerating adoption of AI in healthcare is the mission of Pure Storage and its FlashBlade technology, an all-flash scale-out object-based solution that can expand to petabytes of capacity. As Esteban Rubens says, infrastructure to power AI, machine learning and deep learning needs to be effortless, efficient and evergreen to ensure success today and into the future. Here’s how.

When it comes to teaching new dogs new tricks, radiology training programs need to be thinking about updating their curricula and preparing for both the short- and the long-term effects of AI and machine learning, according to “Toward Augmented Radiologists,” a new commentary published online in March in Academic Radiology.

Ever the visionary, Paul Chang sees AI as an asset to radiologists. As he sees it, “AI and deep learning doesn’t replace us. It frees us to do more valuable work.” The vice chair of radiology informatics at University of Chicago Medicine takes a quick look through the crystal ball at the four stand-out challenges facing radiology with the rise of AI.

To look into the future is to catch only a glimpse inside Simon Warfield’s radiology research lab at Boston Children’s Hospital. His team is pairing hyperfast imaging and deep learning to push the limits of medical imaging and artificial intelligence (AI) to identify, prevent and treat disease. He’s also eyeing ways AI will help as data sharing expands among research sites. “The research world needs to look forward to manage forward,” he says.