RSNA 2025 Reflections: Key Trends Shaping the Future of Medical Imaging

RSNA 2025 wasn’t just a conference; it was a marathon. My step tracker proudly logged over 15,000 steps a day, walking hall to hall, booth to booth, absorbing conversations, demos, and emerging technology. The physical scale of RSNA mirrors the scale of transformation happening in radiology, and every step felt like walking into a future accelerating faster than anyone predicted.

Those miles of carpeted aisles didn’t just exercise the legs, they exercised perspective. With every step, a pattern became more obvious. The giants of imaging - GE, Philips, Siemens, and Canon, displayed even better machines, clearer images, faster acquisition times, but the real buzz wasn’t in the hardware this year.

It was what came after the image was formed, a realization that:

• Radiology is no longer defined by the machine.
• The future is happening beyond the equipment.

1. Radiology Hardware Is Becoming the Smartphone Camera

The future of radiology feels strikingly similar to the evolution of smartphones. Hardware used to be everything. Resolution, sensors, megapixels — the competitive battlefield. But eventually the camera became standardized, and the real differentiation moved into software, apps, processing, and what we do with the image.

Radiology is now standing at that same crossroads. Equipment manufacturers will continue improving image quality, and they should. But increasingly, they resemble camera manufacturers in the phone industry: essential but no longer the full story. The real value, the innovation, and the competition is shifting beyond the machine.

Just as smartphone cameras had plateaued, radiology hardware appears to be reaching a maturity point: the image is a given. The question now is: What intelligence sits on top of that image?
And that’s where this year’s disruption is coming from.

2. AI Is Eating the Edges of Radiology — One Anatomy at a Time

This year, it felt like AI vendors were everywhere. Each one solving something narrow, precise, clinically sharp:  breast cancer grading, liver lesion detection, multimodal prostate biopsy workflow, oncology triage, structured reporting, and more. AI is no longer one big solution. It is modular. It is plug-and-play.

You pick your anatomy. You pick your function. You integrate.

Instead of one-size-fits-all AI solutions, RSNA 2025 featured dozens of highly specialized tools:

•  Breast cancer scoring, prostate-biopsy workflow optimization, liver lesion quantification, lung-nodule detection, stroke triage, structured reporting, and more.
• Each solves a narrow but clinically significant problem  and often does it very well.

And here’s the disruption: these companies don’t need to partner with OEMs to reach the market. They’re going straight to radiologists, hospitals, PACS vendors,  selling intelligence like apps rather than hardware upgrades.

These aren’t bundled with scanners.
They don’t need to be tied to an MRI or CT purchase.
They plug into systems like apps — modular, optional, upgradable.
This shift is already changing how radiology services will be built.

3. PACS — The Emerging AI Marketplace of Radiology

The most powerful booths this year weren’t scanners, they were PACS (Picture Archiving and Communication System)platforms repositioning themselves as AI hubs.
Traditionally, PACS did: store →  retrieve →  view →  report
Now, PACS is evolving into something far more powerful:
 detect anomalies →  quantify →  triage → assist diagnosis → auto-report →  learn over time

AI is no longer installed machine-by-machine, it is delivered through PACS like apps. In effect, PACS is increasingly turning into the “App Store” of radiology. Radiologists will pick and choose which AI-powered modules to “install” regardless of what scanner made the image.

For AI companies, PACS platforms are the new highway.
Instead of selling hospital-by-hospital, scanner-by-scanner, they can now deploy:

1. integration-ready
2. multi-vendor compatible
3. plug-in based AI tools

Radiologists don’t need to call the IT department.
They just enable a feature inside PACS — sometimes with a subscription toggle.
A hospital PACS becomes an AI marketplace.

This is a massive structural shift, radiology is no longer just hardware + image.
It is increasingly workflow + intelligence — and PACS is becoming the gateway.

4. 2025 Is the Year Radiologists Became the Customer

AI companies are no longer selling only to hospitals or OEMs. They are selling directly to radiologists — the people who read, interpret, report, and decide. Why? Because the radiologist now controls:

• efficiency
• throughput
• diagnostic speed
• reporting quality
• case triage
• burnout management.

No longer will imaging be defined just by who owns the scanner. It will be defined by who owns the workflow and the intelligence layered on top of it. Radiologists are no longer just users. They are becoming the primary customers.

5. Compact Radiology Manufacturers – New Kid on the Block

While software and AI are revolutionizing workflows, hardware is also evolving, but not by simply becoming “bigger” or “fancier.” Instead, a growing number of manufacturers are shrinking, simplifying and mobilizing imaging hardware, making imaging more accessible, flexible, and point-of-care friendly.

• Portable / low-field MRI scanners: New players such as Aspect Imaging, Neoscan Solutions and other emerging vendors have announced lightweight or low-field MRI units intended for bedside, ambulatory care, or rural/field-hospital use.
• Next-Generation Compact CT : Phased-array CT concepts with lower radiation and modular design, lightweight, potentially mobile CT systems. Micro-CT units redesigned for ERs, urgent care, and community imaging. CT is being miniaturized and democratized in ways unimaginable a decade ago.
• Handheld & wireless ultrasound : The handheld-ultrasound market is booming. Companies are launching compact, AI-assisted, battery-operated ultrasound probes, many integrating cloud sync and tele-medicine capabilities.

What This All Means — The Road Ahead

Based on what I saw (and walked) in those 15,000 steps, the likely future of radiology looks like this:

1. Hardware commoditizes — becomes smaller, simpler, more mobile
   Scanners, CTs and ultrasound devices will evolve into modular, often portable, point-of-care tools, like smartphones rather than heavy desktops.
2. Intelligence becomes the differentiator — not the magnet strength or slice count
   AI modules, workflow tools, decision-support, automation; that is where the value will be.
3. PACS becomes the “Operating System” or “App Store” of radiology
   Radiologists will “install” what they need — flexible, tailored, upgradable,  regardless of where the image came from.
4. Radiologists become the direct target consumers
   They will choose the tools that speed up workflow, reduce fatigue, improve accuracy, not procurement committees choosing hardware.
5. Imaging becomes more accessible, more distributed
   Small clinics, rural hospitals, ambulances, remote centers, can all join the imaging ecosystem. Radiology won’t be limited to big urban hospitals anymore.

The Shift Is Already Happening

Radiology is evolving into a software-driven, AI-powered, and increasingly mobile ecosystem,
and PACS will be the gateway, while compact hardware will be the enablers.

The next decade will not be about who builds the biggest magnet, it will be about who builds the smartest workflow.

And those who embrace modular hardware + AI + flexible deployment early will shape the next generation of imaging.

About the Author

Srinivas Rao Kudavelly Senior Principal Consultant, Healthcare and Life Sciences

Srinivas has 25+ years of experience spanning Consumer Electronics, Biomedical Instrumentation and Medical Imaging . He has led research and development teams, focused on end-to-end 3D/4D quantification applications and released several "concept to research to market" solutions. He led a cross functional team to drive applied research , product development , human factors team, clinical research, external collaboration and innovation. He has garnered diverse sets of skill sets and problem challenges. and has over 30 Patent filings and 15 Patent Grants across varied domains , mentored over 30+ student projects , been a guide for over 10+ master thesis students ,peer reviewer for papers and an IEEE Senior Member ( 2007)