Training AI to Detect Disease: Stand Up To Cancer’s Julian Adams

**Shervin Khodabandeh** (0:02)
How does AI advance the medical community's ability to detect and fight cancer early? Find out on today's episode.

**Julian Adams** (0:10)
I'm Julian Adams from Stand Up To Cancer, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:18)
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** (0:37)
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** (1:04)
Hey, everyone. Thanks for joining us today. Today, Shervin and I are talking with Julian Adams, President and CEO of Stand Up To Cancer. Julian, thanks for joining us.

**Julian Adams** (1:14)
Glad to be here. It's a pleasure to meet you, Sam.

**Sam Ransbotham** (1:17)
Shervin and I enjoyed learning more about your organization when we met. But for our listeners, can you briefly describe what Stand Up To Cancer is all about?

**Julian Adams** (1:26)
Stand Up To Cancer is a research organization. We're a charity, a non-profit, that raises money to fund cancer research. Primarily, we encourage team research. Our favorite style of research is we call them dream teams. That means multi-institution, many investigators all working on very difficult problems in cancer.

**Shervin Khodabandeh** (1:53)
Do those dream teams ever include any non-humans like AI of some sort?

**Julian Adams** (2:00)
AI is pervasive. We can't not use it. It's one of the tools. Just like a piece of laboratory equipment, it's part of everything we do in terms of getting more accurate and deeper information.
In cancer research, two of the most obvious ways are in radiology. When you get a CT scan or an MRI, that pattern recognition is better seen by a computer than by the human eye. And if you show it 10,000 or 100,000 of those scans, it gets really good at detecting abnormalities. So that's the scanning piece. Now, as you know, in cancer, we often have to take a biopsy and look under the microscope. And there, too, it's about pattern recognition. The microscope magnifies the image. We can stain the tissue of origin and see what's normal, what's abnormal. So you can see the cancer and you can see adjacent cancer. And the computer is very, very good at distinguishing that. Much better than the naked eye. The AI component of this is that the more images you show the computer, the more it learns, the better and more accurate it describes the abnormality.

**Sam Ransbotham** (3:24)
So when I think about projects like ImageNet, so ImageNet is a database of 14 plus million images that are used in, well, it started with a contest about a decade ago for image recognition, and it got really, really good over that last decade. In fact, I think it got to the point where the error rates are about the same as human error rates, or maybe even better than human error rates. How many images does it take to be able to read a CAT scan or MRI or a slide?

**Julian Adams** (3:56)
Depending on the kinds of questions being asked and the level of resolution, you need more images rather than fewer images. But typically, we will start with a training set that may be 500 to 1,000 images. Those will typically be retrospective. So they've already been diagnosed as a cancer. We train the AI algorithm to define that as cancer. And then we have to do prospective studies, and those can number in the tens of thousands, or as you said, perhaps even millions, to get better and better and better. So the beauty of AI is the machine keeps learning. Humans get tired, they get sleepy after a long work shift days, they need another cup of coffee, they can never eventually compete with the computer. There are other applications though in, for example, drug discovery. We used to do drug discovery by making hundreds of thousands of compounds and screening libraries with robotic instruments to see what binds to the receptor in the tissue of interest.
Today, we can do all of that in silico because of programs like AlphaFold, which tells you how proteins fold. Proteins usually are the targets for drugs or antibodies. The folding of those proteins and the three-dimensional structures of those proteins can be predicted by the incredible power of AlphaFold. One can imagine doing a lot of the drug discovery on computers and eventually needing not to make hundreds or thousands of compounds to see which one is the best, but just making maybe a few dozen compounds because the computer can also give you the hierarchical assessment of which compounds fit best. There's a fourth area that I haven't touched on yet, and that is really in the early detection of cancer. And this is something that I am most focused on recently, and that I'm guiding Stand Up To Cancer to pay a great deal of attention to. Let me cite a few facts. If you can detect cancer at stage one, where it's localized, so it's locally advanced, hadn't spread yet, we call that stage one. If you can detect cancer at stage one, you have a 90% chance of curing that cancer, either through surgery, surgery and radiation, or there may be drugs as well used. And what I'm most excited about is the area of therapeutic vaccines, which we can...