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Northwestern just solved radiology’s biggest bottleneck.
Their new generative AI drafts complete radiology reports in real time, and the results are staggering:
📊 15% to 80% efficiency gains across 11 hospitals
🎯 95% complete drafts in each radiologist’s style
⚡ Zero loss in clinical accuracy
🚨 Catches critical findings like pneumothorax with 99.9% specificity
But here’s what makes this different:
While everyone else is building narrow AI for single conditions, Northwestern built a holistic system. It reads the entire X-ray or CT scan and generates a comprehensive report, just like a human radiologist would.
They didn’t use ChatGPT or other internet-trained models. Instead, they built from scratch using their own clinical data, creating a lightweight system that integrates seamlessly with EHRs and reporting software.
The most impressive part? It’s already live in clinical practice.
Some radiologists are seeing 40% productivity gains. Others report clearing backlogs that once seemed insurmountable. The AI even flags life-threatening conditions before radiologists might catch them, potentially saving lives through earlier detection.
This isn’t theoretical anymore. It’s happening right now at Northwestern.
We keep hearing about AI’s potential in healthcare. Northwestern just proved it works.
The question isn’t whether AI will transform radiology. It’s how fast other health systems can catch up.
Because with radiologist shortages getting worse and imaging volumes exploding, we can’t afford to wait. Every delayed report is a delayed diagnosis. Every backlog represents patients waiting in uncertainty.
Northwestern just showed us the solution. Now it’s time for the rest of healthcare to pay attention.
♻️ Repost if radiology departments need AI to survive the imaging volume crisis
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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.
