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Imagine detecting pancreatic cancer 3 years before symptoms appear.
That’s exactly what Mayo Clinic just made possible.
Their new AI tool, REDMOD, just shattered expectations by spotting pancreatic cancer on routine CT scans with 73% accuracy, while human radiologists only caught 39% of the same cases.
Here’s what makes this revolutionary:
The AI doesn’t need special scans. It reads the same routine CTs already sitting in your PACS system.
It’s nearly 3x more sensitive than radiologists when looking 2+ years before diagnosis.
It maintains 90-92% consistency on repeat scans, eliminating the variability we see with human readers.
💡 The game-changer? This turns every abdominal CT into a potential early detection opportunity.
Think about the implications for your imaging center or hospital:
• Every patient getting a CT for kidney stones could be screened for pancreatic cancer
• No additional imaging costs or radiation exposure
• Early detection shifts treatment from palliative to potentially curative
Pancreatic cancer has a 5-year survival rate under 12%. Why? Because we catch it too late.
But when detected early, that survival rate jumps to over 40%.
This isn’t about replacing radiologists. It’s about giving them superpowers to catch what the human eye simply can’t see years in advance.
The precision exceeds UK NICE guidelines by 12x (36% vs 3% threshold), making it clinically viable today, not someday.
For FQHCs and community hospitals serving high-risk populations, this levels the playing field. You don’t need a specialized cancer center to offer cutting-edge early detection anymore.
The real question isn’t whether we’ll adopt AI in radiology.
It’s whether we can afford not to when lives hang in the balance.
♻️ Repost if early cancer detection should be a standard of care, not a luxury.
👉 Follow me, Jonathan Govette, for daily, real-time updates on healthcare technology and business news. LinkedIn Profile: https://www.linkedin.com/in/jonathangovette/
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Author:
Jonathan Govette is a seasoned healthcare and technology executive with more than two decades of experience building, scaling, and advising digital health companies. He is the Co-Founder and CEO of Oatmeal Health, an AI-driven Lung Cancer Screening and Diagnostics company focused on expanding access to early detection for underrepresented populations, particularly patients served by Federally Qualified Health Centers and value-based health plans.
With a background in engineering, product development, and strategic partnerships, Jonathan has founded and led multiple health technology ventures across clinical care delivery, regulated medical software, and AI-enabled diagnostics. His work sits at the intersection of medicine, technology, and health equity, with a consistent focus on translating complex clinical problems into scalable, real-world solutions.
Jonathan has spent much of his professional life dedicated to improving outcomes for marginalized and underserved communities. He has designed and implemented frameworks that align clinical quality, reimbursement, and technology to sustainably advance health equity at scale. This mission is deeply personal and informs his leadership philosophy and long-term vision for healthcare transformation.
In addition to his operating experience, Jonathan is an author and long-time writer in the healthcare domain, with over 20 years of published work covering digital health, medical innovation, and healthcare systems. He is a frequent mentor to early-stage founders and regularly advises startups on product strategy, partnerships, and go-to-market execution in regulated healthcare environments.
Before entering industry full-time, Jonathan nearly pursued a career in medicine with an early path toward cardiothoracic surgery, an experience that continues to shape his clinical perspective and respect for frontline care delivery.
CEO | Oatmeal Health | AI Lung Cancer Startup | Engineer | Writer | Almost Became a Doctor (Cardiac Thoracic Surgeon) | 3x Health Tech Founder | Startup Mentor | Follow to share what I’ve learned along the way.
