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The FDA just received a petition that could change radiology AI forever.
On January 2, 2026, a citizen petition landed on the FDA’s desk proposing something radical: let radiology AI systems update themselves without going through the full regulatory circus every single time.
Here’s what’s happening:
Right now, if you want to improve your FDA-cleared radiology AI algorithm (even just tweaking the model weights based on new data), you need to file a whole new 510(k) submission. It’s like requiring a full DMV test every time you adjust your mirrors.
873 AI tools have FDA clearance for radiology. 115 new ones were added in 2025 alone.
Yet they’re all frozen in time. 🧊
The petition targets Computer-Aided Detection and Diagnosis (CAD) systems that already have 510(k) clearance. Instead of resubmissions for updates, vendors could run postmarket studies to prove safety and effectiveness.
Think about what this means:
• AI that learns from your hospital’s specific patient population
• Algorithms that improve monthly, not yearly
• Real-world performance that actually gets better over time
• Innovation at the speed of technology, not bureaucracy
GE Healthcare has 96 radiology AI clearances. Siemens has 80. Every single one requires a new submission to update.
The irony? The FDA’s own internal systems just deployed agentic AI that can adapt and learn. But the radiology AI protecting patients? Locked in regulatory amber.
This isn’t about loosening safety standards. It’s about recognizing that AI’s greatest strength (its ability to learn and improve) is being completely neutralized by a regulatory framework designed for static medical devices.
The public comment period ends February 27, 2026.
If this passes, we could finally have radiology AI that gets smarter with every scan, not despite regulation, but because of smarter regulation.
The question isn’t whether AI should evolve in healthcare.
It’s whether we’ll let it.
♻️ Repost if radiology AI should be allowed to learn and improve
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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.
