Health IT: Exploring the Role of Technology in Healthcare - Episode 9
Simon D. Murray, MD: Welcome to this HCPLive® Peers and Perspectives presentation titled “Health IT: Exploring the Role of Technology in Healthcare.”
I'm Dr. Simon Murray, an internist from Princeton, New Jersey, and medical director at MJH Life Sciences. I'm joined today by Dr. Eric Daimler, a computer scientist and leading authority in robotics and artificial intelligence with more than 20 years of experience as an entrepreneur, researcher, policy maker, author, and investor. He was named a Presidential Innovative Fellow by the Obama administration and drove the agenda for US leadership in the research, commercialization, and public adoption of AI and robotics. Eric is also co-founder of six tech companies including Conexus. Welcome Eric, let's begin.
Eric Daimler, PhD, MS: Thank You Dr. Murray good to be here.
SM: Well, I'm really pleased to have you here today I've read a lot about you and heard a lot about the things you've done and I am really interested to see what you have to say about artificial intelligence and its application to medicine particularly.
ED: Good to be here, thank you for having me.
SM: I heard one of your videos where you said we should stop teaching trigonometry and algebra and we should be teaching statistics and probability. I agree a hundred percent. So much in medicine of what we do is probability, and so much is distorted in the misunderstanding of statistics and probability. You cited the issues of trying to treat blood pressure and then cited all the car accidents that kill people, you know that that was an analogy I used to use all the time. People would say, “I'm not going to take Lipitor, you know it might kill me” and I would say “you drive your car, well your chances of getting killed in your car are about a hundred times greater than taking Lipitor” right? But people don't understand statistics and a lot of doctors don't understand statistics.
There is an old adage that says: what's 1 plus 1—if you ask a mathematician, “2.” What's 1 plus 1, if you ask a philosopher, “it depends.” If you ask a statistician, what's 1 plus 1, “what do you want it to be?”
So with that, you could tell me what your definition of AI is.
ED: Those are good anecdotes. I have a good friend who went through medical school contemporaneous in my doctoral program and he, as a joke, wrote a paper on his comrades in life sciences, in health care. The title of that paper was “What Physicians Know About Computer Science” and then the paper was blank. That was him doing it a physician and as a computer scientist. But it's a funny relationship we have to physicians. I have been really excited to be interacting with the healthcare community from a lot of different angles and I often start with the definition: you know we think today of AI being electricity or you know “data is the new oil” and much of how then we talk about AI could be analogized to us talking about how the pistons work inside of an internal combustion engine or in electricity talking about Edison and Tesla debating alternating or direct current. You know those might be interesting and we might have a good intellectual conversation about that, but it really does nothing for us in talking about the system that is transportation, the system that is energy distribution, and that's what I'm working to do with bringing people into the conversation around AI.
There is a definition of a robot that I adopt for AI and it's anything that can sense, plan, and act. And then repeat that experience. That's a learning machine: it could be AI, could be robotics, but that's what I think: sense, plan, and act.
And inside that system is a lot of different touchpoints, it's a collection of the data from sensors that could come from any number of different ways, this could be software or hardware, and then into the computation, the planning, where we could be talking about the computation, the storage, the networking -- but not just the minor point of the learning algorithms.
And then we have the output which also can be disregarded or put aside, but the output is important because when we get a result from a computer how do we interpret it and how do we know whether it's right or wrong, you know it and if it gives us something that's 10% away from our expectations we might say well fine, but what if it's 10 times away from our expectations, but it’s right? How do we know? That's an output judgment. And that whole “sense plan act” I think is a great way to start the conversation and frame our whole engagement around AI and modern digital technologies.
SM: The MacDonald definition was way more involved: every aspect of learning or other feature of intelligence can in principle be precisely described as a machine, that a machine can be made to simulate it, an attempt will be made to find out how to make machines use language, form abstractions and concepts of blah blah blah blah blah…I like your definition a lot better.
ED: It’s towards the motivation: are we trying to teach you to be an AI researcher? Then we want to get rather precise and we can say that machine learning is a subset of AI and there are non-machine-learning AIs and deep learning that's in the public discourse as a subset of machine learning, that's all accurate but it's pedantic for the conversation of the general population. And if we if we focus on that too much it takes people away, in they end up just reacting to what the “high priesthood” of AI researchers produce and then we will present it to you and you will like it. I want people to figure out their place in this in this whole system, have a type of system intelligence around it.
SM: Your definition also brings forward AI as the ability for machines to do tasks rather than take jobs. There's a difference. Right what we're trying to do is ask them to perform duties that humans do to make our lives better, we're not asking them to take our jobs because they're not going to take our jobs.
ED: There's a lot of different ways to think about that. These augmentation machines have historically just improved our experience little by little. Tt's easy for us to think of cars in this metaphor when we think about AI and everybody can kind of join in this discussion: the cars we have today are very different than they were a hundred years ago. Little by little week we added brake lights we added turn signals we added airbags, more recently we've added ABS we've added traction control, and then even bringing forward in the last couple of years we've had Distronic cruise control, you know smart cruise control. We've had cars shake, give haptic feedback, and then force cars back into a lane. That's how AI comes into being in most industries, just little by little tasks are taken off of us and automated.
I don't think anybody's going to say their life is worse off for ABS. I mean ABS and traction control are credited with saving tens of thousands of lives just in the United States alone. I think that's how a lot of these technologies are going to express themselves in automated vehicles and I think that's a just a fantastic framework to think about all the applications of AI software or robotics hardware when we're talking about these technologies in the healthcare system.
Transcript edited for clarity.