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.

Eight years ago, Women’s Imaging Associates in Birmingham, Ala., was a small, well-respected mammography practice serving six OB/GYN offices in its area. Today, having embraced a 100% telemedicine model, its three fulltime breast specialists read images for 22 client facilities scattered around the U.S.—not only OB offices but also outpatient imaging centers and hospitals large and small. 

SUNNYVALE, Calif., July 31, 2017 /PRNewswire/ -- Accuray Incorporated ARAY announced today it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its iDMS™ Data Management System, enabling its use with the Company's TomoTherapy® platform. It's designed to integrate with a wide range of technologies, including the Radixact™, TomoTherapy® and CyberKnife® Systems, Accuray Precision™ Treatment Planning System, and third-party treatment planning and oncology information systems.

The Connecticut Hospital Association says it has launched the nation’s first statewide data repository for tracking imaging-related radiation dosage. The repository has the potential to help benchmark appropriate doses from coast to coast, according to an article published June 27 in the American Hospital Association’s Hospitals & Health Networks.

When there’s a near-miss or safety incident (NMSI) with a patient undergoing radiation therapy, and the incident was related to the disease or treatment itself, the lapse often involves imaging. But it’s not imaging per se that raises any particular risk.

Xconomy.com has posted a worthwhile overview and look ahead at AI in healthcare. Its reporter would have been remiss had he not “gone there” with radiology.

Migrating patient imaging data from one computer system to another demands attention to detail, as a healthcare system in Australia is learning in the wake of a mislabeling error affecting several hundred thousand medical images.