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:
- Reflecting on the personal and professional paths that led Bill to create Signos.
- What Signos does for glycemic dysregulation and why it’s so important for healthcare.
- Insight into the role that ML plays in Signos’ technology.
- How Signos trains their ML algorithms using various sources of data.
- Food logging and other challenges that come with gathering CGM data.
- Ways that external factors influence model performance and how Signos mitigates that.
- Qualitative user responses that help Bill measure the impact of this technology.
- Bill’s mission-driven advice for other leaders of AI-powered startups.
- How he believes the impact of Signos will continue to evolve going forward.
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
Links:
Resources for Computer Vision Teams:
LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Custom Vision Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.
You Might Like
