While there is always a risk when you're an early adopter, Stewart says Mercy is careful of overreach.
"There is a way to do this without excessive risk," he says. "Dedicate a small group of people and a limited amount of capital and a time frame to explore. Even if it fails miserably you've contained it and then you're dedicated to learning why it failed. Use very straightforward, well-contained use cases. Don't overgeneralize and think we're going to be experts in machine learning. Take very small use cases that have a high probability of machine learning getting you to areas that human intelligence can't do. Understand that it may be an R&D thing. It may be a 100% loss financially, but you will start to learn what this technology is and the promise it holds and do better your second iteration."
Those on the leading edge of the AI movement see its vast potential for medicine within the decade and sooner, but they take their dose of wonder and hope with a sprinkling of skepticism. AI may someday play a role in curing cancer, but that's not going to happen next week.
"It's appropriate to be cautious. I certainly am," says Isaac Kohane, MD, PhD, Marion V. Nelson professor of biomedical informatics and chairman of the department of biomedical informatics at Harvard Medical School. "Almost 30 years ago, my PhD in computer science focused on the topic of medical applications for artificial intelligence. Back in the day we used to call it expert systems. Those were very clearly overhyped, and they're not being widely used now."
Kohane warns that the promise of AI could be overwhelmed by the hype in the suddenly crowded vendor space. "The loudest talkers may not be the best performers," he says. "If the loudest talkers who are not the best performers get the limelight and they fail, it is going to put the hopes that a lot of us have for this technology at risk—not because the technologies are bad, but because people will lose interest and optimism and a willingness to invest."
Michael Blum, MD, professor of medicine and cardiology and associate vice chancellor for informatics at the University of California–San Francisco (UCSF), says he sees great promise in AI, but that more than 20 years of practicing medicine, and training as an engineer before that, have kept him grounded.
"I have seen many silver bullets that were going to revolutionize medicine, and there have been many well-known, well-hyped technologies that have come before this," he says. "These are all tools that go into the tool kit, and when they are used appropriately with available assets they can sometimes be very effective. But whenever something is getting to be incredibly popular and talked about in the lay press all the time, the likelihood of it truly transforming healthcare probably goes down."
Blum, who also serves as director for the Center for Diagnostic Health Innovation at UCSF, views machine learning at this stage in its development as another source to help clinicians improve outcomes.
"There was a lot of talk that big data was going to transform healthcare as it did other industries, but it turns out that big data is just another tool. Big data will power artificial intelligence development, but in and of itself it is not going to transform healthcare," he says. "Having said that, I am much more optimistic about the capabilities of these technologies than I have been in quite some time in terms of how they are going to transform the way we work. They have the ability to allow mundane and limited complexity tasks to be done by machines already, which allows providers to go to the human side of care, spend more time with patients, and deliver better care without having to worry about a lot of the minutia that the computer can take care of."
John Commins is a content specialist and online news editor for HealthLeaders, a Simplify Compliance brand.