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ARTIFICIAL INTELLIGENCE TAKES ON MEDICAL IMAGING

 

 

Radiologists look at a new image every three to four seconds during an eight-hour workday. That's hardly enough time to find the patterns, abnormalities, and other markers essential in making a diagnosis. Hospitals are hoping to lessen that load by outsourcing some of that work -not to people across the ocean, but rather to machines. These computers, running artificial intelligence and machine-learning algorithms, are trained to find patterns in images, and identify specific anatomical markers. But they also go deeper and spot details the human eye can't catch. Early versions of these algorithms, currently in trials, are both accurate and fast.
 
One potential problem, howver, is how the algorithms are initially trained. Sometimes, the data they're fed in the learning process come from just one specific model of imaging machine. Because different models have different radiation doses and slightly different technologies, “you've got an inherent bias that's built in,” said Steve Tolle, vice president and chief strategist of IBM's Watson Health Imaging. To help avoid that bias, IBM is using a collaborative approach, working with 20 health systems to use images from many different sources to develop its Watson cognitive platforms, which one day, Tolle said, will be able to perform image analytics.
 
Source: Rachel Z. Arndt, Modern Healthcare [7/8/17]  
 
Courtesy of Barry Block, editor of PMP News.  
 
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