por John R. Fischer
, Senior Reporter | August 05, 2019
RadNet has acquired remaining shares of Nulogix, an early stage company focused on bringing AI to radiology, as part of a plan to open a new division for the development of AI technology in diagnostic imaging.
The company is setting its sights on developing, acquiring and investing in machine learning technologies that will aid radiologists in image interpretation and business processing.
“We expect that machine learning, big data applications and automation algorithms will allow us to deliver our services more cost-effectively, efficiently and accurately,” said Dr. Howard Berger, chairman and chief executive officer of RadNet, in a statement. “We are committed to supporting technologies that make our business run in ways to advantage our patients, joint venture partners, referring physician communities and contracted health plans.”
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A nationwide provider of freestanding, fixed-site diagnostic imaging services in the U.S., RadNet recently completed another acquisition, earlier this year, of Kern Radiology Medical Center
. The purchase of the California-based outpatient imaging center provided it with four new officers, as well as existing contracts with Dignity Health, including its Bakersfield Memorial Hospital and Mercy.
RadNet previously paid $2 million in April 2018 for control over 25 percent of Nulogix. It chose to acquire the remaining 75 percent it did not own now to support the setup of its new division.
In addition to internally developing its own solutions, RadNet plans to partner, license and invest in AI technologies developed by other companies.
“Today, we spend almost 20% of our globally-billed net revenue on the radiologist interpretation of our images,” said Berger. “If A.I. were to lower the cost of image interpretation by making our affiliated radiologists more productive and more accurate, all of RadNet’s stakeholders stand to benefit materially.”
Financial details regarding the purchase of the remaining 75 percent of Nulogix were not disclosed.