At RSNA 2017, Much Discussion—and Hype—Around Artificial Intelligence | Healthcare Informatics Magazine | Health IT | Information Technology Skip to content Skip to navigation

At RSNA 2017, Much Discussion—and Hype—Around Artificial Intelligence

November 27, 2017
by Mark Hagland
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Radiologist leaders see AI as essential to helping their specialty to survive and thrive in a value-driven healthcare system

As the annual conference of the Radiological Society of North America (RSNA) opened this week at Chicago’s vast McCormick Place Convention Center, a palpable shift in attention was evident, as the demand for value from radiological healthcare services appeared to accelerate not only in the U.S., but across the advanced industrialized world.

A strong surge in emphasis on artificial intelligence and machine learning was evident everywhere at RSNA 2017, whose attendance, as measured in terms of total advance registration, was almost exactly identical to what it had been in 2016 (at 48,445 this year, compared to 48,888 last year; and with 23,097 professional registrants this year, compared to 23,656 last year). The emphasis was evident in keynote addresses and presentations, and absolutely evident in the exhibit halls (in which 667 exhibitors were present, compared to 691 last year), where large banners and bold signage proclaimed a new era of innovation around new forms of supportive intelligence.

Indeed, the topic of artificial intelligence/machine learning, in the context of transformation of the profession, was an explicit element in the president’s address given on Sunday, Nov. 26, by current RSNA president Richard L. Ehman, M.D. Under the headline, “Is It Time to Reinvent Radiology?” Dr. Ehman, according to the report published Monday morning in the Daily Bulletin, the association’s official publication, told his audience on Sunday that “No other major medical specialty is so profoundly based on a remarkable invention that changed the world. And it is your collective focus on the well-being of our patients,” he told the radiologists gathered at the convention center, “that drives all of our efforts and innovations.”

Dr. Ehman directed some of his remarks to the subject of the need for radiologists to invest in leading-edge radiological research. But he also urged audience members to join in leveraging information technologies and strategies to help demonstrate the value of what radiologists do to enhance overall patient care delivery. “These innovations—whether they are machine learning, highly-focused protocols or value-focused reengineering—may allow our most powerful diagnostic tools to be used even more effectively for the benefit of patients.” What’s more, Ehman said, “Imagine a world in which our most advanced imaging technologies are widely available, modestly priced, and considered high-value, first-line diagnostic tools. Knowing that disruption and reinvention of our practice is inevitable, we must guide the process ourselves instead of having it imposed on us by others outside our discipline.”

A Cresting of the Hype Cycle?

The comments that Dr. Ehman made in his president’s address could scarcely have been imaginable ten years ago, when radiologists were under far less pressure to produce so many studies so quickly, and with such accelerating policy, regulatory, and payment demands as nowadays. Yet the reality now for radiologists is such that there is no alternative but to innovate in order to survive, and to prove their worth in a healthcare system increasingly focused on value, Rasu Shrestha, M.D. told Healthcare Informatics. Dr. Shrestha, a practicing radiologist and the chief innovation officer at the 20-plus-hospital UPMC health system based in Pittsburgh, said in an exclusive interview that the die has long been cast in the direction of radiologists’ needing to very actively prove their value not only to the purchasers and payers of healthcare, but also to the referring physicians with whom they interact on a daily basis. And that, Shrestha said, will necessarily require the investment in, and implementation of, every useful type of artificial intelligence tool and strategy relevant to the optimization of diagnosis, speed and efficiency of diagnostic imaging study development, operational efficiency, and communications with referring physicians.

As a result, Shrestha said, “When I was chair” of RSNA’s Scientific Program Committee (he spent a year as chair, with his term as chairman ending a year ago, though he remains on the committee as a member), “one of the things that I pushed for was embracing machine learning.” Still, he conceded, “I think we’re in the eye of the storm now in terms of the hype cycle. You could see the storm brewing; and we in the imaging industry had a lot to do with that, because it’s a technology whose time has come,” at a moment when the volume of data and information involved in radiological practice is exploding, and the need for speed is accelerating as never before. Radiologists will perforce need to leverage AI tools and strategies simply to stay productively and effective in practice, he believes.

Even so, Shrestha made it clear that he sees a great deal of confusion around terms and concepts in this area. He said that it’s important to distinguish between artificial intelligence, machine learning, and deep learning. He has in fact developed formal definitions for all three phenomena. According to Shrestha, “artificial intelligence is an umbrella term,” referring to “any technique that tries to mimic human intelligence, logic, and inference, using ‘if-then’ rules. Machine learning,” he has written in a presentation, “is a subset of artificial intelligence that includes abstruse statistical techniques that enable machines to improve tasks with experience That category includes deep learning,” which is “a subset of machine learning that is composed of algorithms that permit software to train itself to perform tasks, like speech and image recognition, by exposing multilayered neural networks to vast amounts of data.”

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