Helping Doctors Make Better Decisions With Data: UC Berkeley's Ziad Obermeyer

**Sam Ransbotham** (0:02)
Currently, machine learning researchers have to beg and plead for healthcare data. This scarcity fundamentally limits our progress. What could change when we get open, curated, interesting data?
Find out on today's episode.

**Ziad Obermeyer** (0:17)
I'm Ziad Obermeyer from Berkeley, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:23)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, professor of analytics at Boston College. I'm also the AI and Business Strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:41)
And I'm Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

**Sam Ransbotham** (1:06)
Today, Shervin and I are thrilled to have Ziad Obermeyer joining us.
Ziad, thanks for being here. Welcome.

**Ziad Obermeyer** (1:13)
Thank you. It's wonderful to be here.

**Sam Ransbotham** (1:15)
I got to know Ziad at the NBER conference in Toronto where he was talking about some of his health data platforms work.
So, maybe let's start there, Ziad. Tell us a little bit about what you're doing, what this exciting platform is about. Tell us about Nightingale.

**Ziad Obermeyer** (1:30)
Absolutely. So, I can tell you maybe a little bit about the backstory, which is that all of my research is in some way or another applying machine learning or artificial intelligence to healthcare data.
And even though I say I do research in this area, actually what I spend a lot of my time on is pleading for access to the data that I need to do that research, pleading, wheeling and dealing using all of the networks and contacts that I've accumulated over the years. And it's still just incredibly hard and frustrating. And so, given how much time I was spending on that, one of my co-authors, Sandil Malanathan, who's at University of Chicago, and I decided that we were probably not alone in this pain. And so, a few years ago, thanks to support from Schmidt Futures, Eric Schmidt's Foundation, we were able to launch a nonprofit called Nightingale. Nightingale Open Science, that's its full name, is a nonprofit that uses philanthropic funding to build out interesting data sets in partnership with health systems. We work with health systems to understand the things that are high priority and interesting problems for them to work on, and we build data sets that take massive amounts of imaging. So, chest x-rays, electrocardiogram waveforms, digital pathology, biopsy specimens, and we pair those images with interesting outcomes from the electronic health record and sometimes from social security data when we want mortality.
And we create data sets that are aimed at answering, I think, some of the most interesting and important questions in health and medicine today.
Why do some cancers spread and other cancers don't? Why do some people get a runny nose from COVID and other people end up in the ICU? So, all of these questions, I think, are areas where machine learning can really help, not just help doctors make better decisions, but help drive forward some of the science, but those data sets are in very short supply. We create those data sets with health systems and then we de-identify them and we put them on our cloud platform where we make them available to researchers around the world for free. And I think our inspiration for a lot of that work was the enormous progress in other areas of machine learning, driven by the availability of not just data sets, but open, curated, interesting data sets that take aim at important problems and that are made available for people who want to drive performance forward on some of those tasks. That's one of the health data platforms that I've been working on over the past few years.

**Sam Ransbotham** (4:03)
Yeah. So give us some examples of that. What are some analogies? What are the other platforms you're referring to?

**Ziad Obermeyer** (4:09)
So I think the most famous one of these is called ImageNet. So this was put together a number of years ago by essentially getting a bunch of images from the internet and then getting people to caption those images. So, you know, we get a photo, it's people playing frisbee on the beach.
And then once we've got millions and millions of those images, we can train algorithms that map from the collection of pixels in that image to the caption that a human would assign that image. There are many data sets like that. There's like a handwriting recognition data set, there's a facial recognition data set.