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Blog
Reut Yalon, PhD

Why We Stopped Building Only Algorithms and Started Building Infrastructure

Adoption, C-Suite, Information Technology, System Integration
Table of Contents

Aidoc didn’t start as a platform company. Like many others in the clinical AI industry, we started by building a best-in-class triage tool for radiologists. But very early on, we realized something critical: one algorithm, no matter how good, wasn’t enough to drive meaningful change.

The truth is healthcare doesn’t run on isolated moments — it runs on patterns, workflows and systems. If we wanted AI to be used, it couldn’t just be good. It had to be everywhere. 

So, we evolved. What began as a narrow product became a broader, system-level strategy. Here’s how that shift happened, and why it matters now more than ever.

Early on, we saw the limitations of “one model, one use case.”

Take stroke, for example — a critical use case where AI can save lives. But how often does it present in a typical week? If that’s the only AI a health system has deployed, usage stays low and low usage doesn’t drive familiarity, trust or outcomes.

From the beginning, we pushed for breadth. We built AI for the chest, the abdomen and the head, so radiologists across modalities and care settings would engage with it throughout their day. That diversity of use didn’t just drive adoption, it also improved our algorithms, user interface and product thinking. It taught us what it really takes to operate AI in a clinical environment.

That’s when the real shift happened: We stopped thinking in algorithms and started building infrastructure.

What started as a collection of AI models evolved into something more foundational. We realized that to make AI work at scale, we needed to build three core components alongside the algorithms:

  • A set of smart data and AI layers to handle integration of multiple data types, data normalization, AI analysis and AI monitoring 
  • A combination of dedicated user interfaces that fit different specialties (e.g., desktop app for radiologists, mobile app for interventionalists and patient management platform for outpatient clinics) together with integration into native systems like PACS and EHRs
  • Measurement tools to enable data transparency on three major vectors: AI performance, user adoption and clinical value  

This was the birth of our platform, the aiOS™.

Real-world experience is what taught us how to build a platform.

We didn’t just imagine what a clinical AI platform should look like. We built the aiOS™, piece by piece, by deploying our own solutions in hundreds of hospitals, learning what works and adjusting accordingly.

That’s also why we’re able to support multimodality and multispecialty use cases  — something most AI marketplaces can’t do. A typical marketplace vendor builds one-off integrations for each new AI. One for CT chest, another for X-ray legs, another for MR head. The burden piles up on IT, while adoption stays siloed.

By contrast, the aiOS™ is a consolidated infrastructure for imaging and EHR data from day one, so onboarding a new use case doesn’t mean starting from scratch.

Marketplaces weren’t built for care coordination.

Today, many vendors claim to offer “end-to-end” solutions. But in a marketplace model, “end-to-end” usually means cobbling together different vendors that don’t talk to each other. One company for detection, another for tracking and a third for triage alerts. It’s fragmented by design. Patient care isn’t fragmented. 

Let’s say you’re managing brain aneurysms. With the aiOS™, one system handles awareness, tracking, follow-up and care team coordination — across both radiology and patient management workflows. That’s a single system, a shared dataset and a unified experience for clinicians.

That kind of continuity is what lets you scale clinical AI beyond imaging and algorithms.

Algorithms don’t scale. Platforms do.

An algorithm pilot can be a good starting point, especially if a health system is new to AI, but you can’t pilot your way to maturity. If the infrastructure isn’t in place to support AI across departments, data sources and workflows, even the best initial use case won’t translate to enterprise value.

The truth: AI doesn’t fail because of the algorithm. It fails because the system isn’t built to support it.

We built aiOS™ to fix that — not with more tools, but with the right foundation. Because in clinical AI, it’s not about what you can deploy. It’s about what you can scale.

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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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