What if healthcare could predict your future instead of just treating your present?
In this episode, I’m joined by Michael Geer, co-founder of Humanity, to explore how AI is transforming healthcare from reactive treatment to predictive prevention.
Michael and I discuss how large biological models, similar to large language models like ChatGPT, are making it possible to predict future health outcomes with unprecedented accuracy. We explore biological age versus chronological age, why personalized health recommendations will replace one-size-fits-all advice, and how AI literally saved Michael from permanent nerve damage.
This conversation gets into the weeds of transformers, attention mechanisms, and the future of medicine, but don’t worry, we keep it accessible and practical.
Key topics: biological age, large biological models, AI in healthcare, predictive medicine, personalized health, digital twins, preventive care, longevity science
About the guest: Michael Geer is co-founder of Humanity, a company building tools to measure and reverse biological aging. Before Humanity, he helped grow Badoo to over 300 million users. Connect with Michael at mg@humanity.email or learn more at humanity.health
Timestamps:
00:00 Introduction to Predictive Healthcare
02:28 The Motivation Behind Humanity
05:39 The Shift from Reactive to Predictive Healthcare
08:05 Understanding Biological Data and Predictions
12:00 The Importance of Context in Health Predictions
16:35 The Role of Doctors in a Predictive Framework
21:42 Open Source Biological Models and Their Impact
24:25 Digital Twins and Future of Treatment Predictions
26:16 The Impact of Health Data on Treatment Predictions
27:58 Exploring Longevity and Biological Age Predictions
31:06 Modeling Interventions and Supplements for Health
35:07 The Role of AI in Personalized Medicine
39:06 The Future of Healthcare with Predictive Models
Links
#80: 🎙️ INTERVIEW | Your Rate of Aging with Michael Geer, Co-Founder of Humanity
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