Longitudinal imaging improves accuracy

Daniel describes how serial imaging and incorporating past scans dramatically lowers false positives and clarifies incidental findings.

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Most of medicine is built around snapshots. You feel something, you test for it, and by the time you find it, you’re already behind. But what if the problem isn’t the test—it’s how we use it? In this episode, I sit down with physicist and imaging pioneer Dr. Daniel Sodickson, Chief Medical Scientist at Function Health and author of The Future of Seeing. We break down why tools like MRI are shifting from one-time scans to something far more powerful: tracking your health over time. Watch the full conversation on YouTube, or listen wherever you get your podcasts. In this episode, we cover: • Why waiting for symptoms puts you behind—and how to get ahead • What an MRI can reveal about your body that bloodwork can’t • How tracking your health over time helps you catch problems sooner • Why having a baseline could change the way you make health decisions • What it means to shift from reacting to disease to actually predicting it When you stop looking at a single result and start looking at patterns, you can catch changes earlier, reduce false alarms, and better predict where your health is headed. View Show Notes From This Episode

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(0:00) Introduction and overview of modern medical imaging

(3:26) Discussion with Dr. Daniel K. Sodickson begins

(3:45) Full body MRIs: Benefits, risks, and the inspiration behind "The Future of Seeing"

(7:52) Extending senses and paradigm shifts in imaging technology

(14:55) Longitudinal imaging and its benefits

(19:17) Future of personalized health data and imaging technology

(23:54) Addressing information overload and reducing false positives through AI

(28:33) Cost, accessibility, and innovations in imaging techniques

(32:00) Vision for ubiquitous and continuous health scanning

(33:30) Imaging vs. blood work: Comprehensive health assessment

(35:29) Real-life examples and early detection through imaging

(39:27) Historical context and real-time health data collection