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Blog
Reut Yalon, PhD

What Sparked the aiOS™ Platform, and Why It’s Difficult for Others to Replicate

Care Coordination, System Integration, Workflow
Table of Contents

The aiOS™ platform didn’t begin with a grand vision, rather it started with a problem (actually, several of them):

  • Chest CTs with contrast produced inconsistent results
  • Head and neck scans failed unless slice thickness and timing were exactly right
  • Some hospitals struggled to get any AI results on certain scans at all — and when they did, they weren’t always reliable

Then came the broader challenges: limited IT bandwidth, splintered workflows, disconnected user interfaces and growing pressure to prove clinical AI was working.

Point solutions couldn’t keep up. Worse, they couldn’t even start without a heroic integration effort.

That was the moment Aidoc stopped building isolated algorithms and started building infrastructure. What emerged became aiOS™, the first operating system for clinical AI, and still the only one designed from the ground up to normalize fragmented data, monitor real-world performance, drive clinical action and scale across an enterprise.

This enables a truly unified workflow: radiologists work within a single, streamlined interface, while tailored interfaces extend access to non-radiologists. All are seamlessly connected, ensuring consistent communication and coordinated action across the entire care team.

The Technical “Aha” Moment

Most health systems don’t realize how inconsistent their data is until they try running AI across it. We saw firsthand how small variations — like CT slice thickness or timing delays — could tank performance. Instead of blaming the data or narrowing use cases, we built around the variability.

That led to three early breakthroughs:

  • A normalization layer to align data across imaging protocols and hardware types
  • A data logic layer to interpret nuanced clinical parameters
  • A monitoring layer that flags when performance might drift 

Together, these formed the foundation for intelligent orchestration, a system that determines which AI to run, on what data and when. 

More than a technical fix, this became the first of four layers that now define the aiOS™ platform: Run AI, Drive Action, Measure Impact and Scale Use Cases.

What Our Customers Told Us (and What They Didn’t Have To)

While we were building, health systems were asking a new question: How do we know this is working?

They weren’t just asking about accuracy. They wanted to understand:

  • User engagement 
  • Performance over time
  • Downstream clinical impact on their own data

They also told us they didn’t have time to manage five vendors or chase down five different integration teams. From the start, we engineered aiOS™ for efficiency — simplifying adoption with a single integration into health system IT.

AI That Works Around Clinicians, Not the Other Way Around

Success with AI isn’t just about accuracy. It’s about adoption, and that depends on workflow. From day one, we prioritized native integration. The user interface had to work with existing PACS, RIS and electronic health record (EHR) systems. 

When AI is truly integrated and orchestrated, something powerful happens:

  • Improved disease awareness: More incidental findings can surface.
  • Improved outcomes: More patients can be identified for treatment.
  • Improved efficiency: More workflows and encounters are touched by AI.

What Breaks Without a Platform

We’ve seen what happens when health systems try to scale AI without infrastructure:

  • Performance starts inconsistent and gets worse over time
  • Clinicians disengage due to disconnected workflows from numerous vendors
  • IT resources are stretched thin
  • Leadership can’t quantify value and momentum stalls

aiOS™ solves these problems because it was built to. It wasn’t retrofitted or stitched together. It was purpose-built for the realities of enterprise healthcare: fragmented data, limited resources, high clinical stakes and the need for repeatable, scalable impact.

Prepared for the Next Era of Clinical AI

Today, aiOS™ runs across some of the largest health systems in the U.S. 

It powers real-time care decisions in radiology, cardiology, neurology and beyond. Plus it continues to evolve — now supporting Aidoc’s foundation model — to expand clinical coverage and accelerate AI development.

Still, the core mission of Aidoc hasn’t changed: reduce diagnostic errors and improve patient outcomes. We didn’t build aiOS™ to run more algorithms. We built it to transform care — at scale.

Interested in learning more about aiOS™?
Book a demo.

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Reut Yalon, PhD
Reut Yalon, PhD, is the Chief Product Officer at Aidoc, leading Aidoc’s product group and the company’s product strategy. Since joining Aidoc, Dr. Yalon and her team developed and commercialized dozens of clinical, FDA-cleared products that are widely used daily in healthcare centers around the world, based on a market-leading overarching framework that enables rapid deployment of new products to the market. Prior to joining Aidoc, she held product positions in medical imaging start-ups, with a passion to build products from ideation to commercialization. Dr. Yalon received her post-doctorate and PhD from the Weizmann Institute of Science, where she developed novel imaging methods for non-invasive prenatal clinical applications. A prolific scientist, Dr. Yalon authored over 10 scientific papers and two book chapters.
Reut Yalon, PhD
Chief Product Officer

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