Continuous glucose monitors (CGMs) are a trusted tool for diabetics, but today’s guest believes that widespread adoption could also be valuable for reversing the obesity crisis. Meet Bill Tancer, the Co-founder and Chief Data Scientist of Signos, a metabolic health platform that combines CGMs with a unique AI engine to offer real-time data and recommendations for healthy weight management.
Today, Bill joins me to talk about all things metabolic health and machine learning. Tune in as we discuss how the Signos team trains their machine learning algorithms, the challenges they encounter when it comes to gathering data, and some of the other external factors that influence the performance of their model. We also touch on the value of qualitative data in the form of user feedback, the importance of keeping your mission in mind in the rapidly expanding AI space, and so much more! To find out how Signos is unlocking metabolic health with ML, don’t miss this episode of Impact AI.
Key Points:
Quotes:
“Along with diabetes as its own health risk, having [dysregulated] glucose can lead to other medical problems. Cardiovascular disease, stroke, Alzheimer's, just to name a few. [It] is such an important goal for [Signos] to help people reduce their glycemic variability.” — Bill Tancer
“That's what gets me up in the morning; hearing [positive user anecdotes]. That, in conjunction with looking at our own data and how our members are improving in terms of their wellness, tells us we're having a measurable impact.” — Bill Tancer
“It is so easy [with] all the things you can do with AI to end up in a space where you've got a solution that's searching for a problem to solve. The antidote to finding yourself in that situation is always returning back to your mission.” — Bill Tancer
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