pass muster with the FDA. But Clario and CuraCloud think their first application in triage—despite having a major potential impact on patients—will have “a relatively low regulatory hurdle,” Wood says.
“It’s not like you’re really changing anything other than how fast it’s read. But then again, if you’re that person with a pulmonary embolism, the difference between five hours and 15 minutes—it could be life or death,” Wood says.
Applications in which the creator of an A.I. system makes a specific, numerical claim about a system’s efficacy or performance—it will automatically detect a certain percentage of tumors, say—will likely trigger at least the FDA’s 510(k) clearance process to determine whether a medical device is equivalent to something else already on the market, or the more-rigorous premarket approval (PMA) process for medical devices, potentially including clinical trials. That would be a significantly more expensive regulatory pathway.
“The companies that have the biggest budgets will probably go for the PMA so they can get the strongest claim,” Wood says. “A lot of the startups out there will not spend the money to get a giant, strong claim.”
He expects a scattershot approach until the FDA provides guidance on how it will regulate A.I. technologies in healthcare, as part of a broader focus on digital health.
The regulatory requirements—and therefore the costs—of developing A.I. systems for healthcare raise another question: Who’s going to pay for them?
In the U.S., there’s no billing code for triaging radiology scans, Wood notes. “The reason people will buy this is to just provide better patient care,” he says.
And that could be the key: Large, private radiology practices compete for hospital contracts. “The one that has A.I. might win the contract because they can talk about prioritization,” he says. “And they can talk about how it results in better patient care. So all other things being equal, that will help a private practice win business. … [Radiologists] won’t get paid a dime more for using this technology, so it’s only this competitive nature of the practices that really makes it possible for us to even go to market right now.”
CuraCloud, for its part, has more ambitious plans down the road. The company was formed in late 2015 and includes a team of longtime collaborators—many originally from China who earned computer science PhDs from U.S. universities—led by CEO Qi Song, Butler says. The company’s road map includes pre-populating radiology reports and computer-aided detection and diagnosis—an area where Seattle has something of a pedigree, adding to its core strengths in cloud computing, A.I., biomedical research, and medical devices.
Seattle-based Confirma, for example, was a leader in the development of computer-aided detection software in applications including mammography, and Wood was previously its vice president of research and development. Merge Healthcare acquired Confirma in 2009 for about $22 million. Merge was scooped up by IBM in 2015 for $1 billion and incorporated into the IBM Watson Health business.
Clario is also looking at applications in pathology, which is undergoing its own digital transformation akin to radiology’s transition away from film. In April the FDA permitted Philips to market a system that enables pathologists to review and interpret digital surgical pathology slides from biopsied tissue—a first for the industry.
“Because the system digitizes slides that would otherwise be stored in physical files, it also provides a streamlined slide storage and retrieval system that may ultimately help make critical health information available to pathologists, other health care professionals, and patients faster,” said the FDA’s Alberto Gutierrez in a news release announcing the approval.
Sounds familiar.
Of course, it’s still a long road from the triage application CuraCloud and Clario are working on now to an A.I. radiologist that can diagnose and write reports.
Wood and Butler diverge on the timeline by which such a system could be realized, with Butler being more bullish.
And, if that system does arrive, will insurers or the Centers for Medicaid and Medicare Services, which sets reimbursement policies for a large share of U.S. healthcare spending, pay a computer for a service that used to be performed by a human—assuming that it was at least equal to the quality provided by a human? “My guess is they won’t,” Wood says.
But if an A.I. radiologist could competently read routine chest X-rays as well as or better than a human for half the fee—admittedly, a big if—“then obviously the payers are going to love it,” he adds.
That would suggest a bleak future for radiology jobs. Add it to the list of occupations across the economy from truck drivers to journalists to accountants threatened by A.I. Not so fast, say Butler and Wood.
Demand for radiology is growing, driven in large part by demographics, they argue. “People are getting older, especially the baby boomers, and you get a lot of images later in life,” Wood says. There’s also demand from outside the U.S.; many countries have far fewer resources to train sub-specialists in radiology and related fields, Butler says.
Data compiled by the Harvey L. Neiman Health Policy Institute show the number of radiologists in the U.S. increasing significantly on a per-capita basis, from 10.6 per 100,000 people in 1995 to 12.3 per 100,000 in 2013.
Wood thinks automated evaluation of routine radiographic scans would not only increase efficiency, it would also improve job satisfaction among radiologists who are sometimes “a little bored by the easy stuff.”
Butler points out that there’s no radiologist at most dentists’ offices, but dental X-rays are routinely performed and interpreted there.
He says radiologists would just as soon pass the quotidian broken arms and chest X-rays off to a competent machine because “they don’t get paid much for it, and they’re not practicing at the top of their license to look at some of these routine things that even a computer could figure out.”
CuraCloud is not out to replace radiologists, he adds. “Our goal is to help them work faster, more efficiently, with higher quality,” he says. “We’re going to go for those things that have the highest value to the radiologist and to the broader healthcare delivery system.”
Photo credit: X-ray photo by Flickr user Erich Ferdinand, cropped and used under a CC BY 2.0 license.
