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Lung-RADS was never designed for AI. That’s the problem.
For years, we’ve used a 1-to-4 categorical scoring system to decide who needs follow-up after a chest CT. It works. But it was built for human radiologists reading films in isolation, not for AI systems processing thousands of scans with probabilistic outputs.
Here’s the tension nobody talks about:
Lung-RADS generates a false positive rate of 18% to 40+%. That sounds like a statistics footnote. It isn’t. In the National Lung Screening Trial, 1.7% of all screened patients underwent an invasive procedure for a lesion that turned out to be benign. Each unnecessary biopsy carries real clinical risk: pneumothorax, hemorrhage, infection. And real psychological cost: months of patient anxiety waiting on a result that was never cancer.
Scale that across 16 – 28 million eligible Americans and you’re not talking about edge cases. You’re talking about a structural flaw in how we triage.
And it cuts both ways. The same categorical system that over-refers on false positives misses roughly 10% of real cancers entirely.
AI changes the math.
Oatmeal Health’s LungAI outputs a continuous 0-100 malignancy probability score, not a category. At equivalent sensitivity to Lung-RADS, it reduces false positives by one-third: specificity improves from 82% to 88%. At equivalent specificity, it catches 3% more cancers Lung-RADS would miss. (Pre-FDA Cleareance) Launching soon.
That number travels with the patient into the EHR, into the care navigation workflow, and into the shared decision-making conversation.
Why does this matter for FQHCs and safety-net clinics?
Because the patients most likely to fall through the cracks are uninsured or Medicaid patients at community health centers. Their PCP is managing 2,000 patients. A nodule flagged for 3-month follow-up gets lost in the noise. A probability score of 7.2% routes differently than a Lung-RADS 3 label. High-probability findings get acted on. Lower-probability findings get appropriately scheduled. The nodule doesn’t disappear because a category label undersold the risk.
Stage I lung cancer has a 77% five-year survival rate. Stage IV has 9%. The difference is almost always a follow-up that happened or didn’t.
We keep building better detection tools. We’re still terrible at the follow-through.
The next frontier in lung cancer screening is not finding more nodules. It’s making sure the right people act on the ones we already find.
Are your workflows ready for a world where every chest CT comes back with a malignancy probability score, not just a Lung-RADS category?
♻️ Repost if AI should replace categorical nodule scoring with continuous probability scores before another Stage IV diagnosis happens at follow-up.
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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.
