Machine Learning: Opportunity for the clinical setting and consumer
- [Interviewer] Hi everyone, welcome to AI in Focus. A two part Podcast series where industry experts talk about artificial intelligence, it's challenges, benefits and future. I'm here with Atul Butte, Director of the Institute for Computational Health Sciences at UC San Francisco to talk about the challenges and benefits of using AI in a clinical setting. Thanks for being here, Atul.
- [Atul] It's great to be here.
- [Interviewer] So Atul, what do you think are the primary challenges in the practical use of artificial intelligence in the clinical setting?
- [Atul] Everyone's talking about the use of artificial intelligence in medicine right now. Indeed, the Food and Drug administration, the FDA, has already approved six devices and software tools in just the past 18 months. So, I think we're getting to more practical use. But they have to be very specific uses. So, for example, the approved uses include things like stroke triage in emergency rooms, diagnosing diabetic retinopathy from retinal pictures, so very targeted uses. I think we're gonna see more of those in the next couple years where physicians, start up companies, large companies, are gonna go after these very targeted, really high risk, really hard to diagnose, different aspects of medicine, and solve them with computers. I think we're getting there.
- [Interviewer] That's great to hear! Where do you see the next big inroads for artificial intelligence over the next 12 to 24 months?
- [Atul] I see a lot of inroads being made from the field of artificial intelligence machine learning in just the next year or two. Certainly, I think we have much more data and getting more data and access to data, I think it's gonna happen. We have health systems now, that are really moving onto standard electronic health record systems, and they wanna do something with that data. As I often say, I think electronic health record data is the most expensive data in America. We're paying physicians to type all this in, we have to do something with that. It's irresponsible if we don't use that data to improve the practice of medicine. So I see more companies trying to partner with some of these health systems, all trying to get into this health care machine learning space. Trying to help physicians and health care practitioners with some aspects of their job.
- [Interviewer] You know, I've always wondered, hanging out in Silicon Valley, what are some of the trends happening there that we may not be aware of?
- [Atul] Yeah, so I'm really lucky I get to hang out in, I think one of the most amazing places in the United States, in Silicon Valley. We certainly have a lot of small and large companies working in the health care space now, and more coming in every week, every month. One aspect of data science and machine learning, I don't think people have paid enough attention to is, the empowerment of patients with all this data. I think that's once major aspect that we are all missing as payers, as providers, as pharma device manufacturers. I don't think we're really that used to patients being empowered with their data for that to actually be happening. And I think those patients are going to be empowered. Not with just the direct access to their data, but with interpretations and advice given through AI on that data. So that's something I'm going to be paying more attention to in the future.
- [Interviewer] Thanks for being here, Atul. We really appreciated having you.
- [Atul] Thank you.
- [Interviewer] We invite you to stay in touch on AI topics by visiting our website optum.com/IQ
Atul Butte, MD, PhD, distinguished professor and chief data scientist, University of California, shares his perspective on the applied use of AI in medicine, the responsibility to leverage the data, and the growing voice of consumers.