Can AI Help to Save the Practice of Radiology for the Future? | Mark Hagland | Healthcare Blogs Skip to content Skip to navigation

Can AI Help to Save the Practice of Radiology for the Future?

December 5, 2017
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The discussions around artificial intelligence at last week’s RSNA Conference spoke to the numerous needs for the technology, as radiology lurches forward into the new healthcare

In what was perhaps one of the most memorable openings in literature in English, Charles Dickens began his immortal A Tale of Two Cities with this: “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of light, it was the season of darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct o heaven, we were all going direct the other way—in short, the period was so far like the present period, that some of its noisiest authorities insisted on its being received, for good or for evil, in the superlative degree of comparison only.” And yes, that was one long, run-on sentence….!

And yes, participating in RSNA 2017, this year’s edition of the annual RSNA Conference (sponsored by the Oak Brook, Ill.-based Radiological Society of North America), did bring to mind Dickens’ astonishing opening to his great 1859 novel.

And though I saw no one at RSNA 2017 who reminded me at all of Sydney Carton, Lucie Manette, Charles Darnay, or Madame Defarge, I did actually think a bit about France in 1775 (on the eve of the French Revolution). Here’s the thing: the practice of radiology, as we’ve all known it, is moving into uncharted territory now, as the financial, operational, and medical practice model on which it’s been based, is shifting under the feet of today’s radiologists. With both Medicare and private-insurer payment under accelerating threat (let’s face it, diagnostic imaging procedures are an easy target for reimbursement deficit-hawk types), and with the demands for speed of turnaround for interpretive reports also accelerating, there are literally not enough hours in the day for practicing radiologists to make up growing income shortfalls from ongoing reductions in payment from all sources.

And really—let’s be real—none of us are going to be seeing radiologists standing on street corners attempting to sell $1 homeless-resident newspapers anytime soon. In 2016, the median compensation for non-interventional radiologists in the U.S. was $503,255, according to the American Medical Group Association (AMGA), as reported by RSNA; that was up from $490,399 in 2015.

Indeed, RSNA’s Richard Dargan, in his story, quoted Howard Forman, M.D., a professor of radiology, public health, economics and management at Yale University, as stating that “The take-home message here is that we are faster and better at reading studies, and we’ve improved the way we deliver images and the way we process reports and communicate results. There’s no reason to think that this will change anytime soon,” Dr. Forman said.

But, be that as it may, radiologists do feel themselves under threat, as fee-for-service medicine gradually begins to collapse, and radiologists feel more and more need to prove their value in the new, value-based, healthcare. That challenge was the subject of so many conversations at RSNA this year; and every single radiologist I spoke to agreed that radiologists need to begin to seriously leverage artificial intelligence/machine learning/deep learning technologies and strategies in order to demonstrate their value in the emerging healthcare.

Multiple uses envisioned for AI in radiology

In that context, on Friday, Dec. 1, Mia DeFino published an interesting article in Diagnostic Imaging, entitled “Learning from Deep Learning in Radiology,” which highlighted the AI/deep learning emphasis at this year’s RSNA. As DeFino pointed out, “There are many opportunities to use AI and deep learning in medical imaging: image quality control, imaging triage, efficient image creation, computer-aided detection, computer aided-classification, and automatic report drafting.” And she reported on a presentation by Lucio Prevedello, M.D., of The Ohio State University, who told his audience that “[D]eep learning can help in other ways aside from helping label images, such as improving process efficiency when dealing with many high priority cases. For example,” DeFino noted of Dr. Prevedello, “[H]is lab has been able to filter incoming images based on priority using deep learning. The algorithm looks at the images to identify brain hemorrhage or stroke, if the computer detects one of the flagged factors, the patient will move up on the priority list to have their images analyzed first. If the algorithm does not detect any critical factors, the patient’s case falls towards the bottom of the priority list.”

That is just one of numerous potential ways in which radiologists might leverage AI capabilities. Citing another initiative, this one involving Curtis Langlotz, M.D., Ph.D. of Stanford University, DeFino wrote, “For example, at Stanford, Langlotz described a deep learning algorithm that can improve MRI image quality and suggested a future where the MRI machine can notify the technologist that the images are too fuzzy to be read accurately. Through this type of approach, it is possible to improve MRI image quality and have the patient spend less time in the machine.”

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