Artificial Intelligence

Deep learning-based convolutional neural networks (CNNs) can help radiologists select musculoskeletal MRI protocols, according to a study published by the Journal of Digital Imaging.

The FDA is working to encourage the use of artificial intelligence (AI) technologies in healthcare, according the prepared remarks by the agency’s commissioner, Scott Gottlieb, MD, at Health Datapalooza in Washington, D.C.

The profession of radiology may rightly regard 2017 as an extended coming-out party for AI within the specialty. At ACR’s annual meeting in May, the keynote speeches all revolved around the changes AI will bring. AI occupied an entire quadrant of space, including a dedicated stage, at the RSNA annual meeting in the fall. Seemingly dozens of startups, along with numerous established companies, lined up in vendor booths ready to dazzle you with the next generation of radiology technology.

With few exceptions, the most attention-demanding discussions about how and when artificial intelligence will transform radiology have been led by—and largely held within—the academic sector. That’s not surprising, given that teaching radiologists are the ones doing the research, blazing the trails and comparing the notes.

Machine learning might be the next step in predicting patient wait times and appointment delays—factors crucial to healthcare’s quadruple aim and its emphasis on quality of care—in radiology practices, researchers have reported in the Journal of the American College of Radiology.

A robotic needle-placement system outfitted with correction software to improve its accuracy in mechanizing computed tomography (CT)-guided needle placement, according to a study published ahead of print in the Journal of Vascular and Interventional Radiology.

Densitas, a medical device company based out of Halifax, Nova Scotia, Canada, announced that DENSITAS|density, its software that uses machine learning to produce breast density reports, has gained FDA clearance.

Elad Walach, founder and CEO of the medical imaging company Aidoc, is one of many in the industry who believes radiology will be transformed by artificial intelligence (AI) sooner rather than later. He went into detail on the topic in a new column published in Forbes.

Radiology supercomputer “Project Clara” could improve imaging quality while speeding up the detection of fatal diseases like cancer and heart failure, Forbes has reported.

Despite radiology’s love-hate relationship with artificial intelligence (AI), advancements could afford the field an opportunity to “hit refresh” and reinvent itself, Emory University professor and radiologist Srini Tridandapani, PhD, MD, MSCR, wrote in Academic Radiology this month.

As online learning options for radiology continue to grow, some students are turning to Second Life—a virtual community developed by its own users and reigned by avatars—to complete their medical education, researchers in Malaga, Spain, have found.

Brainomix, a U.K.-based medical imaging company focused on artificial intelligence (AI), announced Tuesday, April 3, that it has secured $9.8 million (£7 million) to help market its software for treating stroke victims throughout the world.