More than six billion dollars: That’s how much health care providers and consumers will be spending every year on artificial intelligence tools by 2021—a tenfold increase from today—according to a new report from research firm Frost & Sullivan. (Specifically, it will be a growth from $633.8 million in 2014 to $6,662.2 million in 2021.)
AI will be everywhere—from diagnosing cancer to providing weight-loss coaching, says Venkat Rajan, who has the great title of global director for the company’s Visionary Healthcare Program. “Prior to 2015, most of what was happening was sort of academic: pilot programs, exploratory, proof of concept-type stuff,” he says. “And now you’re actually seeing commercial usage.”
AI’s ability to sort through scads of information, and remember everything it has ever seen, could enable a digital (and congenial) version of Dr. House, the brilliant diagnostician from the eponymous TV show, says Rajan. “At first, it’s a complete mystery, it could be one of ten different things,” he says, about the process in the show, and real life, called differential diagnosis. “And then he’s able to sort through various issues, you know, illuminate certain factors on why it’s not one of these other conditions, and he’s able to pull something from memory that figures out ultimately what it is, and they can provide the appropriate treatment.”
Robots won’t steal doctors’ jobs, says Rajan, but they will spare overworked docs some of the dangerous fatigue that can lead to mistakes. “They’re stressed, they’ve got a million different things they’re looking at, so [there’s] stuff they might have missed.”
Other staffers, such as almost-doctor nurse practitioners might do the initial workup before a specialist comes to review the results and make a call about how to proceed. AI could be especially helpful for health care facilities that can’t afford a Dr. House, says Rajan; places that have, for example, a general cardiologist rather than a team with different subspecialties. This will “democratize” diagnosis and care, he says.
Computer-aided diagnosis can weigh more factors than a doctor could on their own, such as reviewing all of a patient’s history in an instant and weighing risk factors such as age, previous diseases, and residence (if it’s in a heavily polluted area) to come up with a short list of possible diagnoses, even a percent confidence rating that it’s disease X or syndrome Y. Much of this involves processing what’s called “unstructured data,” such as notes from previous exams, scan images, or photos. Taking a first pass on x-rays and other radiology scans is one of the big applications for AI that Frost & Sullivan expects.
Computer-aided diagnosis and treatment are already being tried at 16 cancer institutes working with IBM’s Watson Health artificial intelligence venture (which launched in April 2015). The Cleveland Clinic, Columbia University, the University of Kansas Cancer Center, and Yale Cancer Center are among those using Watson to process a patient’s data, including their genetic sequence, and make recommendations. The system’s point-and-click interface is like a decision tree that lists possible diagnoses, recommended tests to further explore the diagnoses, and possible treatment regimens (such as medication plans). It even flags studies, articles, and clinical trials that a doctor might want to look at. “Based on the information that you have in front of you, doctor, here’s a confidence level of, let’s say, five different potential conditions,” says Bill Evans, the CMO of Watson Health, explaining how it might work. “To get that confidence level higher or lower, here’s a set of tests that you should probably run.”
“Right now what you see is that IBM and Watson is at the center of the [health care AI] ecosystem,” says Rajan. However, some artificial intelligence competitors are emerging, such as New Jersey-based startup Hindsait, founded by former Accenture managing director Pinaki Dasgupta. Hindsait’s A.I. was developed specifically for health care.
Watson Health has grown quickly by partnering up. “Mostly what you see is IBM Watson as a sort of platform, and on that are companies who have built, you know, specific solutions that leverage the sort of deep-learning system,” says Rajan. In announcing Watson Health last spring, IBM opened its application programming interfaces (APIs) for clients to plug directly into the analytics engine. (IBM recently also opened Watson APIs for “Internet of Things” sensors and data .) “In one sense, it separates [IBM] from the liabilities and risks of health care,” says Rajan. But Watson will keep going deeper into the medical field, says Evans. “We certainly see the potential of creating our own offerings around specific categories, diseases, et cetera,” he says.
Not all the applications of medical AI will be for doctors. A lot of the growth will be in helping people manage their own health—whether it’s caring for a chronic condition like diabetes or figuring out a plan to lose weight. At CES, IBM announced three new consumer-focused partnerships. Under Armour will use Watson to power a “cognitive coaching system” in an app that provides customized advice for fitness and health. Its wisdom will come through crunching data from Under Armour’s 160 million-member Connected Fitness community. Medtronic will use Watson to analyze data from its insulin pumps and glucose meters, which it claims can warn people up to three hours ahead of an oncoming blood sugar crash. Pathway Genomics is developing an app that will provide customized health advice based on a user’s specific genetic makeup.
“It’s one level to make the diagnosis, but another to support the patient,” says Rajan, noting that one of AI’s benefits is to help the patients stay healthy so they don’t need a doctor as often. The promise, he says, is in, “being able to understand you better and provide you better feedback and guidance and support.”