Artificial Intelligence

Artificial intelligence, machine learning find role in radiology

Artificial intelligence, machine learning find role in radiology

Artificial intelligence and machine learning capabilities are beginning to make an impact within radiology, as vendors start rolling out initiatives to assist professionals in making diagnoses.

The radiology profession is ripe for technology—as radiologists deal with an increasing number of images and bear more responsibility in the clinical process.

For example, radiologists now are being called upon to determine what course of treatment might be less invasive, thus reducing cost, patient recovery times and the risk of complications. Or they typically are asked to assess the rate of progression of a disease such as cancer, to determine what course of treatment is most appropriate.

The use of advanced technology could be considered disruptive and perhaps threatening to some radiologists, but it will become essential for professionals to do their jobs effectively, says Leo Wolansky, neuroradiologist and professor of Radiology at University Hospitals, Cleveland.

“The role of the radiologist has changed so much,” said Wolansky at the recent annual meeting of the Radiological Society of North America in Chicago. “It used to be that we were just asked to distinguish black from white. Now, we’re asked to tell referring physicians what is the percentage of white and black and gray, or how much that percentage has changed over the years. There is criteria for what to do and when to intervene based on what we tell them.”

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Because of the growing need for accuracy in assessments, applications that can assess images becomes important, Wolansky adds.

“With software, we can see changes in diseases such as multiple sclerosis over time,” he says. “”If a patient comes back with more lesions, that can impact how the patient is treated. That’s something software can do—it can compare images and find a new lesion, or indicate where a lesion might be. So if a followup scan shows an increase in lesions, that could have an important implication for treatment.

“There’s an explosion of information confronting radiologists, and there’s a need for speed in processing it for patient care,” he adds. “With stroke, the window of opportunity to effectively treat the patient means an assessment has to be done in minutes, and radiologists need to look at images and be able to make a diagnosis instantly.” That’s where technology can assist, he says.

Vendors are beginning to factor in artificial intelligence and other capabilities to assist imaging professionals in their efforts.

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