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Lisa Chamoff , Contributing Reporter
For hospitals and imaging centers to adopt artificial intelligence (AI) with open arms, they need to become part of the day-to-day radiology workflow, benefiting patients and offering facilities a return on investment.

Those were the main points brought up by a panel of industry leaders speaking during a session titled “Assessing Artificial Intelligence (AI) and Machine Learning (ML) in Imaging IT” at the SIIM annual meeting at Maryland’s National Harbor.

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The panel, moderated by Dr. Khan Siddiqui, co-founder and chief technology officer of higi, included Moshe Becker, founder and chief executive officer of RadLogics; Kevin Lev, senior global product manager at Philips; Esteban Rubens, global enterprise imaging principal at Pure Storage; and Florent Saint-Clair, executive vice president at Dicom Systems.

Saint-Clair spoke about the opportunity for correlating genomics data, radiology images and pathology data for true precision medicine. He also stressed that patient outcomes should be the top priority.

“The patient ultimately is the reason we’re doing all this,” Saint-Clair said. “Letting the patients somehow reap the benefit of all this AI is, to me, at least today, still in the realm of science fiction. … There’s this tsunami of AI. How do you actually operationalize this? It’s not just a matter of creating the AI. How can we get the physicians to take advantage of it? Do they want to take advantage of it?”

Lev, of Philips, said companies can go broader with using AI for things other than image analysis.

“Those are important too, but I think at the end of the day one of the ways that we can try and deliver the greatest amount of value is to be able to interconnect little bits of data and go broader than just image analysis, take a look at patient record data, stuff from the EMR as well,” Lev said. “To not just answer a particular critical question but find out the best way to move the patient from one end of the care pathway all the way to the end."

“For instance,” Lev continued, “if we take a look at a little bit broader than imaging informatics, if you are going to scan a patient and you know down the line they may have to go through an MR cardiac analysis … wouldn’t it be great if the scanner already knew, for instance, that that patient was going to have to undergo some form of analysis, and we do a background check to see if the scan was of sufficient quality using AI, knowing what would happen later on? That way you can help reduce costs overall [and] you could reduce the amount of rescans.”

Bringing up the bottom line, Becker, of RadLogics, said it’s important to discuss how AI will help facilities, and not just the manufacturers, make money.

“I don’t see reimbursements actually going up,” Becker said. “I don’t see new reimbursement codes being introduced to these kinds of solutions. You have to deal with the same zero-sum game of reimbursement, and still show value good enough for somebody to pay you for these kinds of solutions.”

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