Creating a digital twin of a person through new imaging technology allows for tracking health changes over time, potentially leading to forecastable healthcare outcomes.
Integrating high-dimensional imaging data with genetics, biochemistry, vitals, and medical history enables the development of comprehensive models to detect potential health risks and effectively allocate healthcare resources.
Deep dives
Advancements in Human Imaging and Health Forecasting
In this podcast episode, the host interviews Jeffrey Cadets, founder of multiple companies, including Q-Bias, about his journey in building a new kind of imaging technology. After a car accident, Cadets became obsessed with understanding how MRIs work and realized the unscientific nature of current MRI technology. He applied this insight to create a new kind of imaging that generates a digital twin of a person, enabling the tracking of health changes over time. The goal is to develop large biological models (LBM) of humans that can accurately predict health outcomes, with the potential for personalized healthcare agents and forecasting capabilities rivaling weather predictions.
The Importance of Objective Measurements and Multimodal Data
One of the key insights discussed in the podcast is the need for objective measurements and the integration of multimodal data in healthcare. Traditional imaging and diagnostic methods often rely on subjective interpretations and low-dimensional representations, limiting their accuracy and ability to detect complex patterns. By combining high-dimensional imaging data with genetics, biochemistry, vitals, and medical history, it becomes possible to create more comprehensive and accurate models of individuals. This approach enables the identification of potential health risks and the ability to prioritize healthcare resources effectively.
Challenges and Opportunities in Preventive Care
The podcast explores the challenges and opportunities in preventive care. The current healthcare system primarily focuses on addressing health issues once symptoms arise, which often results in limited treatment options. With the development of advanced imaging technology and the ability to track changes over time, there is significant potential for detecting health issues before symptoms occur. However, complete automation of diagnostics may not be realistic, and the key is to use AI and technology to assist healthcare providers in prioritizing their time and resources efficiently. By leveraging high-dimensional data and temporal information, it is possible to identify early signs and patterns that may indicate future health risks.
After his own health scare, Jeffrey got obsessed with how MRIs work—and decided that they were too unscientific to really track human health over time. In response, he built a new kind of imaging, creating a “digital twin” that can be tracked over time. And if health can be tracked over time, in theory it could eventually become as forecastable as the weather.
Join Jeffrey and Vijay as they talk about human imaging, health forecasting, and how a digital twin could change healthcare.
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