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Podcast: Implementing AI Is Simpler Than You Think


Author and entrepreneur Joshua Gans joins Data Book to pinpoint the healthcare pain points where AI is best suited to improve efficiencies and outcomes.

Image via www.vpnsrus.com

When someone says “artificial intelligence” (AI), what comes to mind? Is it a sentient computer, an anthropomorphic robot, or an amorphous data cloud? For many people, the term is firmly rooted in the far-out world of murky futurism—and that’s problematic.

You see, AI is already here. And despite the hype surrounding the complicated things it might do, the things it’s actually doing are pretty straightforward. AI is predicting the next word you’ll text to your kids based on the language you’ve used before. It’s predicting the appropriate results of your search query based on the clicks that followed millions of similar searches. And it’s helping physicians predict diagnoses of disease based on input data from hundreds of thousands of previous diagnoses.

>> READ: Using AI to Spot Diabetic Retinopathy

Do you see a pattern? In the here and now, AI is all about prediction. It is not currently (but never say never!) the panacea that’ll save the world from itself, nor is it the arbiter of the end of things. If you can separate the noise from the news, you’ll see that it’s a simple digital tool that improves our ability to make predictions.

That’s extremely valuable in the world of healthcare, where uncertainty looms around every corner. Many c-suites stew over questions like: How can I identify the patients who are most likely to fall out of their hospital beds? Which patients are most likely to return to the emergency department within the next 90 days? How can I identify proliferative diabetic retinopathy from thousands and thousands of retinal scans without having to manually thumb through each one?

Getting to the bottom of these questions would require prohibitive levels of human power, but AI makes them a much easier lift. So how can hospital executives implement AI so that it improves outcomes, increases efficiencies, and minimally disrupts physician workflow?

This week on Data Book, we speak with Joshua Gans, the Skoll Chair in Innovation & Entrepreneurship at the Rotman School of Management at the University of Toronto, to help our listeners get a better idea. Gans is one of three authors of "Prediction Machines: The Simple Economics of Artificial Intelligence" — a new book geared toward helping executives get a firm grip on AI and accomplish a feat that has eluded many: putting it to good use.


Ep. 18: Can Outside Disruption Save Healthcare?

Ep. 17: Can Healthcare Disrupt Itself?

Ep. 16: Why We Need to Discuss Gene Editing and Ethics

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