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How AI can change radiology practice for the better

by Sean Ruck , Contributing Editor
From the January/February issue of DOTmed HealthCare Business News magazine

After three decades in practice, NYU Langone Medical Center’s chairman of Radiology, Dr. Michael Recht, has gained substantial insight into what the radiology field gets right and what it could improve. That’s why shifting focus to radiology AI a few years back was a natural progression.

Recht is exploring how artificial intelligence can be used to benefit patients and practitioners and ultimately, change how radiology is practiced. The change would rely on technology, but in Recht’s opinion, it’s needed because of technology too.

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“I think what I would tell you is when I went into radiology, it was really the center of what you did in medicine. So in all my rotations, when we had questions, we would go down to radiology, look at the films, talk to the radiologists, and we just knew those were the people who could give us a lot of the answers,” he recalled.

Radiologists weren’t just providing information on what was going on with patients he said, but they would also give direction as to what to do next from an imaging point of view, but times have changed and in part due to the introduction of PACS, there’s a perception that there’s not much of a need to visit with radiologists face to face.

Today, when Recht interviews radiology resident applicants, he always asks what kind of experience they have on radiology rounds. They usually answer that they very rarely had rounds or they ask him if he means with an actual radiologist or just reviewing the films in the ward with the attending physicians on their service. “I think that’s really sad and I think AI has a chance to change how we practice radiology and how we integrate with the clinical care teams by allowing us to do a lot of what we do more efficiently and better, freeing up our time from tasks that we currently do, but really don’t need to do. Allowing us to do the things that really drew us into radiology,” he said.

When healthcare AI started, it was about pattern recognition and whether it could really make the diagnosis. Since those days, it’s become clear that AI can impact every aspect of the image chain, from making sure people order the appropriate test, to ensuring exams are protocoled correctly, to data mining the EMR for a more complete picture to use in patient treatment. AI will also be used for quantitative measurements. “I don’t think many radiologists went into radiology to use a ruler or electronic calipers, so this will be welcomed,” Recht said. AI may help to create reports that are patient-friendly instead of most current reports that are more physician-focused.
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