As AI develops, both the opportunities and the challenges become clear
“We are now at the point where innovation is at the algorithmic level.”
So said Dr. Lloyd Minor, dean of the Stanford University School of Medicine, recently, in an interview with the Wall Street Journal. What he’s referring to is the rapidly growing role of artificial intelligence (AI) across the healthcare sector, a role Stanford has obviously played no small role in facilitating.
Since Minor’s arrival in 2012, the Stanford med school has has established a department of biomedical data science and has struck a data science partnership with Google, both of which are part of an effort “to move beyond personalized medicine to the concept of personalized health, which focuses more on prediction and prevention, according to Dr. Minor.”
What’s interesting about Dr. Minor’s description of Stanford’s efforts is the insistence on the practical side of medical innovation, rather than the more “futuristic” developments such as genomics. For example, he describes a project in which his school teamed up “with the folks in computer science to develop an AI-based system that can look at the hand-washing stations outside of an ICU room and determine when a person has washed their hands. The interlock on the patient’s room is only opened and triggered when a person has gone through the appropriate hand washing. Now, it can be overridden if there is an emergency. But if it is overridden it gets recorded and there needs to be an explanation of why it was overridden. I was skeptical at first. I thought you could fool it by … not actually rubbing your hands. Well, it turns out to be pretty darn good.”
Now, that’s not to say Stanford isn’t forging ahead on the “futuristic” front. “We of course do precision medicine,” Minor said, “applying genomics and Big Data science to the treatment of severe and acute diseases like cancer and heart disease. . . . But we have a different vision and a vision we hope will be significantly more impactful in the intermediate to the long run. And that is precision health. The goal is to be predictive, preventive, and to cure precisely when disease occurs. It begins really with prediction.”
To wit, Minor described a collaborative project with Duke University called “Baseline,” which aims to enroll 10,000 people in a study focused on understanding the interplay between genomics and lifestyle and behavioral factors. By documenting “everything we can imagine about their health measures – whole genome sequencing, a complex immune panel. There will be a new generation of wearables that the volunteers in this study will use.,” researchers hope to be able to track the subjects longitudinally over time and, “for the first time, have enough baseline data so that when something comes up later on, when can look back and say well there might have been an early predictor of that.”
But it’s that predictive capacity of AI that some stakeholders say could also prove somewhat troublesome for providers, at least in the near term. Writing to potential AI investors at MedCity News, venture capitalist Kapila Ratnam notes that “from a clinical perspective, the biggest challenge that physicians are going to face – and in fact are already experiencing – is that they are no longer solely in charge of delivering medical care. Today, they have to answer to the patient, who is sometimes armed with more knowledge about their condition than the specialist. And tomorrow, they may have to respond to an AI application that actually does know more than the specialist.”
Still, Ratnam explains, many studies “have demonstrated that in some cases it’s best to leave the human body to heal itself and that medical intervention is not always appropriate. This becomes even more critical when the use of AI leads to accurate diagnoses earlier in the lifecycle of a disease. At some point, human judgment is a lot more valuable than any insights AI can provide.”
As it turns out, then, the potential specificity of AI may, ironically, be what keeps doctors as relevant as ever.