Artificial intelligence has a long road ahead to reach the front lines of healthcare—but it’s coming.
Big companies and startup investors are pouring billions of dollars into A.I. technologies for healthcare, but a lot needs to happen before such technologies become common tools used by doctors, nurses, and other caregivers. To get there, companies will not only have to spend time and money honing their products and convincing regulators, healthcare organizations, and patients that the tools are useful and reliable, but they must also navigate concerns about job automation and questions about how algorithms and other A.I. tools work.
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In 2014, companies worldwide generated an estimated $633.8 million in revenues from healthcare-related A.I. products, according to a Frost & Sullivan report from January 2016. The report predicted that the market will expand to about $6.6 billion by 2021, at a compound annual growth rate of 40 percent. If the estimate proves accurate, that’s a noteworthy growth pace, but still relatively small dollars for healthcare; consider that Partners HealthCare alone spent $1.2 billion implementing an electronic health records system across its network of New England hospitals and clinics.
Another way to gauge the health of the emerging market is to consider how much hospitals are spending on A.I. tools. Michael Greeley, a partner with Boston-based healthcare technology investor Flare Capital Partners, says his firm has met with dozens of startups who inked “relatively small” deals—around $500,000 to $1 million apiece—with hospitals to use their A.I.-related products on a trial basis. But he says he doesn’t hear about many purchases in the $5 million to $10 million range.
“That’s when you know a market has arrived,” he says—when it’s “real dollars, not just pilot dollars.”
Still, A.I. technology holds real promise for healthcare, according to doctors, entrepreneurs, researchers, technologists, corporate executives, and other industry observers interviewed by Xconomy. They say it’s more a question of when—rather than if—machine learning algorithms and other A.I. tools will be embedded in the day-to-day routine of caring for patients. Some experts estimate the technologies could become widely used within three years, at least in certain areas like medical imaging. But it might take at least a decade, some say, for A.I. to broadly permeate healthcare.
Now the race is on between companies big and small to deliver on that potential (see table of examples below). IBM is an early leader with its Cambridge, MA-based Watson Health business. Big Blue is certainly one of the most aggressive companies in the industry, having spent over $4 billion in the past two years acquiring healthcare-computing companies and building out its A.I. capabilities.
|Other Players in Healthcare A.I.|
But another long-standing, giant American corporation—General Electric—has also set its sights on becoming a leader in A.I. and healthcare. Over the past year, the Boston-based company’s healthcare business has announced partnerships with three high-profile medical care and research institutions to co-develop A.I. software and other digital tools to help with patient diagnosis and other aspects of healthcare.
IBM and GE, along with their competitors, are on a collision course to try to win the future of this fast-moving field. Their products and business strategies may differ, but their efforts could go a long way toward determining how and when machine learning technologies get accepted by patients, doctors, and regulators—and what the consequences will be for the industry.
Big Blue bets big
IBM turned heads in 2011 when its Watson supercomputer handily beat two of Jeopardy’s top human contestants in the televised trivia game show, thanks to its ability to understand language and speech, comb through a vast repository of information, and spit out answers (in the form of questions) in less than three seconds.
Since then, IBM has bet much of its future on Watson, with the idea that the kinds of algorithms and analytics tools that won a trivia contest are also useful for businesses and other organizations. IBM is applying its “cognitive computing” technologies in areas such as commerce, education, financial services, and marketing. But healthcare has been its biggest bet so far.
IBM formed the Watson Health business in 2015 and quickly built it into a more than 7,000-person operation with new headquarters in Cambridge’s Kendall Square neighborhood. Many of those employees were added via acquisition. In the past two years, IBM acquired Ann Arbor, MI-based Truven Health Analytics for $2.6 billion; Chicago-based medical imaging firm Merge Healthcare for $1 billion; Dallas-based population health company Phytel; and Cleveland-based healthcare intelligence firm Explorys. (The prices of the last two deals weren’t disclosed, but they were certainly smaller than the other two.)
But it’s unclear whether those moves are translating to business success, since IBM’s public financial reports don’t break out Watson Health’s revenues. The company says Watson software is being used or implemented by around a dozen of the largest life sciences companies, and its oncology tools are being used or implemented by more than 55 hospitals and healthcare organizations worldwide.
Watson Health’s pitch is that it can quickly sift through reams of data to help customers perform drug research, make diagnoses, and so forth. An IBM spokeswoman says Watson Health’s cloud-based data repository includes 40 million research documents (think medical journals and textbooks), 100 million electronic health records, 200 million healthcare claims records, and 30 billion medical images. (The patient records are stripped of personally identifying information to protect privacy.)
IBM says there is growing evidence that Watson Health’s tools are having—or could have—an impact in cancer treatment and other areas of healthcare.
In an analysis of 1,000 cancer patient cases at the University of North Carolina’s Lineberger Comprehensive Cancer Center, for example, Watson software surfaced new information that could point to potential treatments that doctors hadn’t previously identified in about 30 percent of those cases. Meanwhile, Barrow Neurological Institute used Watson technology to help identify five genes associated with ALS that hadn’t previously been linked to the disease. Without the software, researchers predicted, those discoveries could have taken years instead of a few months, IBM says.
And, in early June, IBM touted a new pilot study with Novartis and Highlands Oncology Group that found Watson technology helped shorten the time needed to screen patients for clinical trial eligibility. During the 16-week study, Watson assessed the eligibility of 2,620 lung and breast cancer patients and reduced the screening time from one hour and 50 minutes to 24 minutes, IBM says.
“There are many examples” of Watson’s healthcare impact, says Kyu Rhee, a medical doctor and IBM’s chief health officer. “And I think in the next five to 10 years, a system like Watson will be part of every health and healthcare decision.”
But the time frame might end up being longer than that, in part because … Next Page »