Most health systems already have enough clinical data to make more useful primary observations than any group of humans could ever connect the proverbial dots on. The good news is that machines are here to help—and they’re getting better at it all the time. 

LungDirect is First in Class to Utilize Ultra-Fast Cloud Computing, Advanced Visualization, and Deep Learning

STAMFORD, Conn.—FUJIFILM Medical Systems U.S.A., Inc., a leading provider of medical informatics solutions, introduces support for DICOM web services including QIDO-RS and WADO-RS in its Synapse® Vendor Neutral Archive (VNA) system with the launch of the latest version 6.4. Synapse VNA® is an enterprise-wide medical information and image management solution that serves as a vendor-neutral, scalable, and organizationally aware storage and distribution system for DICOM and non-DICOM objects. The upgraded Synapse® VNA 6.4 is designed to be a fast and extensible solution for web-based data access that supports RESTful web service implementations of DICOM query (QIDO-RS) and retrieve (WADO-RS) protocols.

A novel computer-aided diagnosis (CAD) algorithm has bested three un-aided thoracic radiologists at predicting malignancy in small lung nodules found on low-dose CT imaging, according to a study published online Aug. 5 in Radiology.

The radiology department at an urban academic medical center has cut its average outpatient CT wait times to a little over an hour, and that includes the scan. Prior to starting the project that took aim at streamlining CT processes, the department was averaging more than three hours in arrival-through-completion times.

Knowing the amyloid status of patients who have symptoms of mild cognitive impairment or dementia affects diagnosis and alters care management, according to the authors of a study running online in Dementia and Geriatric Cognitive Disorders.

Seven months ago, the tech-investor website Nanalyze presented a list of nine AI startups intent on disrupting radiology. Its list has now topped 40. And its editors are more bullish than ever on the prospect of machines pushing radiologists to “move on to more value-added activities.”

Projections of radiology’s demise at the hands of algorithms have been greatly exaggerated. In fact, not only will machine learning not take rads’ jobs: It will become a routine component of their clinical practice, making their work more efficient, accurate, satisfying and valued.