The conference organizers say over 200 companies were in the dedicated AI Showcase alone.
I just got back from RSNA 2025 in Chicago, and if there's one takeaway from walking those massive exhibit halls, it's this: everything is "AI." Seriously—every booth, every banner, every demo station had the letters "AI" somewhere prominent. The conference organizers say over 200 companies were in the dedicated AI Showcase alone.
But here's what struck me after dozens of conversations: most people couldn't actually tell you what makes one AI solution different from another. I'd ask vendors, "Okay, but what makes yours unique?" and the answers got pretty fuzzy pretty fast. Everyone's detecting nodules. Everyone's improving workflow. Everyone's using deep learning. The market has gotten crowded enough that genuine differentiation is hard to find, and I think that's telling us something about where we are as an industry.
To be fair, there were bright spots. Opportunistic screening—where AI extracts additional findings from routine scans, like coronary calcium scores from chest CTs—represents practical value. And the growing use of LLMs for catching speech recognition errors in reports is genuinely useful. But these workflow-oriented applications got far less booth space than the hundredth nodule detector.
United Imaging Growth and Scanner Variability at RSNA 2025
One company impossible to ignore was United Imaging. The Chinese manufacturer has become a real competitor to Siemens, GE, and Philips—and the numbers back it up. They now have over 400 installations across 70% of US states, and just announced they're tripling their Houston production footprint to meet demand. At RSNA they showcased their FDA-cleared 5T MRI, a new ultrasound product family, and their "Native AI" approach of building intelligence into hardware from day one.
When your study sites include not just the big three but also United Imaging systems with different acquisition characteristics, harmonization gets harder.
For clinical trials, this growth has a flip side: more scanner variability. When your study sites include not just the big three but also United Imaging systems with different acquisition characteristics, harmonization gets harder. It's a good problem to have—more competition drives innovation—but it adds another layer of complexity to data management that most AI tools aren't designed to handle.
Why Multi-Site Imaging Clinical Trials Remain Operationally Complex
Anyone who works in imaging clinical trials knows this already, but RSNA was a good reminder: we still haven't figured out how to manage multi-site imaging data without pulling our hair out. You've got 12-15 centers across different countries, different scanners, inevitable protocol deviations, and someone has to harmonize all of that.
AI can spot an anomaly in a brain scan, sure, but it's not going to fix the fact that your site in Boston is using a different acquisition protocol than your site in Barcelona.
The operational stuff—patient enrollment, protocol adherence, data transfer between sites—that's where studies still get bogged down. We don't talk about it as much because it's not sexy, but it's where the real bottlenecks are.
Why Global Radiology Collaboration Raises Multi-Site Coordination Demands
Something I've always valued about radiology: it's genuinely international. You can't make breakthroughs in medical imaging without collaboration across borders. The international nature of RSNA - researchers from Germany, clinicians from Japan, and engineers from Brazil - shows exactly what makes multi-site coordination so critical and so hard. Progress happens when institutions share data and expertise, not when they work in isolation.
The conversations I had reinforced how much the field depends on that global perspective.
The Imaging Operations Problem AI Still Hasn't Solved
This year's RSNA theme was "Imaging the Individual"—and there's real promise there. Photon-counting CT with spectral analysis tailored to specific patients. AI models that adapt to individual anatomy. But here's the irony: the more we individualize imaging, the harder multi-site coordination becomes.
Anyone in clinical trials knows this pain. A dozen sites across four continents, different scanners, inevitable protocol deviations—and now growing equipment diversity as United Imaging gains ground alongside Siemens, GE, and Philips. Someone has to harmonize all of that before any analysis can happen.
AI can spot an anomaly in a brain scan, but it won't fix the fact that your Boston site uses a different protocol than Barcelona, and one just installed a scanner with completely different acquisition characteristics.
We've gotten very good at analyzing individual images. What we haven't solved is coordination.
We've gotten very good at analyzing individual images. What we haven't solved is coordination: getting images collected consistently across 20 sites, making sure data flows where it needs to go, keeping trials on schedule when centers use different EMRs and five scanner vendors. That's where studies actually get bogged down—not in the analysis, but in the operational chaos upstream.
Those problems don't get solved by better segmentation algorithms. They get solved by better systems thinking and better software for how medical imaging actually works.
Those problems don't get solved by better segmentation algorithms. They get solved by better systems thinking and better software for how medical imaging actually works.
Flying home, I'm optimistic about where the technology is headed, but I also think we need to redirect some of that AI energy toward the problems that are actually holding us back. Sometimes the hardest problems aren't the most exciting ones.
Frequently Asked Questions
What was the main takeaway from RSNA 2025?
Despite AI's dominant presence at RSNA 2025, the most pressing unresolved challenges in medical imaging remain operational. Coordination, standardization, and multi-site data management are still harder problems than image-level analysis.
Why does scanner variability matter in clinical trials?
As more scanner vendors and hardware configurations enter the market, achieving harmonization across study sites becomes increasingly difficult. Imaging trials depend on consistency across locations, protocols, and acquisition environments — and variability anywhere in that chain threatens data integrity.
What has AI still not solved in medical imaging?
AI has not solved the coordination problem. Getting images collected consistently, transferred correctly, and managed reliably across many sites, systems, and vendors remains an open challenge despite significant advances in image-level analysis.
Why are multi-site imaging trials so difficult to manage?
Multi-site trials face a compounding set of operational burdens: different scanners, protocol deviations, site-to-site variability, data transfer complexity, and international coordination requirements. These bottlenecks accumulate long before image analysis even begins.
Why does radiology's international nature matter for clinical research?
Radiology is a global discipline that depends on cross-border data sharing and institutional collaboration. That makes strong multi-site coordination essential — progress in imaging research requires sites to share data and expertise rather than operate in isolation.
What AI applications in radiology are showing practical value?
Workflow-oriented applications are delivering the clearest near-term value. Opportunistic screening and large language models that catch speech recognition errors in radiology reports are two examples where AI is proving useful in real clinical settings.
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About the author: Paulo Rodrigues, PhD, CTO and Co-Founder of QMENTA
Paulo Rodrigues is CTO and Co-Founder of QMENTA, a medical imaging AI company providing cloud-based solutions for clinical trials and research.
