Machine learning is one of the hottest topics in radiology and all of healthcare, but reading the latest and greatest ML research can be difficult, even for experienced medical professionals. A new analysis written by a team at Northern Ireland’s Belfast City Hospital and published in the American Journal of Roentgenology was written with that very problem in mind.

A compilation of the latest news in AI and machine learning

As an integrated health-delivery network comprising 13 hospital campuses, two research centers and a health plan with more than half a million subscribers sitting atop the biggest biobank with whole exome (DNA) sequence data in existence, Pennsylvania’s Geisinger Health System is one of the best-positioned institutions in the U.S. to explore the possibilities and initial successes of AI in healthcare. The institution is bringing complex algorithmic concepts to everyday patient care and showing others the path forward.

Artificial and augmented intelligence are driving the future of medical imaging. Tectonic is the only way to describe the trend. And medical imaging is at the right place at the right time. Imaging stands to get better, stronger, faster and more efficient thanks to artificial intelligence, including machine learning, deep learning, convolutional neural networks and natural language processing. So why is medical imaging ripe for AI? Check out the opportunities and hear what experts have to say—and see what you should be doing now if you haven’t already started.

PACS is powering better workflow in breast imaging, transforming the way breast imaging radiologists read studies and interact with one another by improving physician efficiency, accuracy and saving time. Metrics matter in healthcare today and now excellent efficiency, productivity, quality of care and provider and patient satisfaction are measures of success that belong together in the pursuit of better breast imaging.

Cleveland is yet again blazing new ground in healthcare. This time, myriad health systems are actively collaborating to share images. A first for the U.S., we believe. University Hospitals Health System (UH) is leading the charge that now includes more than two dozen hospitals, providers sites and health systems and counting. Here’s how they did it.

Having been in the Sectra PACS fold since 2004, members of the radiology department at six-hospital CoxHealth in Springfield, Mo., didn’t need much convincing to “VNAble” their existing system so it could handle cardiology workflows on top of their own.

When Josh Gluck joined Pure Storage this past April, he arrived well-acquainted with the most pressing data-management issues affecting healthcare IT leaders today. 

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.

Mark Michalski, MD, Executive Director of the MGH/BWH Center for Clinical Data Science gets to see, touch or hear about much of what’s happening in artificial intelligence.

There are the believers in augmented medicine, with physicians and machines working hand in hand and improving care and patient outcomes. And there are the stalwarts who see machines taking over the tasks of mankind. Period.

Population health is absolutely something we want to target. To do that, we are using our archive of images that includes radiology, cardiovascular, interoperative and dermatology. For example, we’re looking at body composition—the amount of muscle, visceral fat and superficial fat. And common sense makes sense. Body composition correlates with how well patients do. In some cases, abdominal fat can even be an early biomarker of some cancers, like pancreatic cancer.