Everyone agrees AI has potential. But the health systems that succeed are ones who invest in the infrastructure to scale it. Real clinical AI isn’t about algorithms alone. It’s about how they’re run, where they show up and how their impact is measured.
We’ve spent years building our aiOS™ platform with four layers that do more than surface AI results. It orchestrates them. It monitors them. It makes them usable across the enterprise. Here’s what that looks like, and why each layer is critical to success.
At the core of any platform is the ability to ingest, normalize and orchestrate data across imaging, EHR and clinical systems. That sounds simple. It’s not. You also need to integrate data across different modalities using smart tools that reduce IT effort. Beyond orchestration, the platform must be able to run AI on that data — intelligently and at scale — and monitor performance over time to ensure algorithms remain accurate, consistent and clinically relevant.
Different systems structure data in different ways. Within a single health system, glucose levels might be measured in different units. Imaging descriptions may not reflect the true content of a scan. You need a way to understand the data — not just pull it in — and then apply logic to determine what should be analyzed, when and by which algorithm.
We built this logic ourselves because we had to. Marketplaces typically rely on each vendor to solve this independently, which creates a heavy burden on IT. If a health system wants to deploy 20 different solutions from 20 different vendors, that’s 20 separate data integration projects. It’s just not feasible.
Aidoc does the heavy lifting once, and then makes it available across every use case — with data orchestration, AI analysis and continuous performance monitoring all built into the infrastructure.
Infrastructure Insight: A true clinical AI platform must orchestrate and analyze data in real-time, with built-in monitoring to ensure accuracy over time. Without that, you’re left with disconnected tools that can’t scale or support timely clinical decisions.
AI only works if it fits within the existing workflow. That’s why we’ve invested deeply in two directions:
All of this is consolidated into a unified workflow. You don’t need 10 different apps to review 10 different findings. We make sure the experience adjusts to the clinician’s role, not the other way around.
Marketplaces, by contrast, offer tools with separate interfaces, timelines and alert mechanisms. I can’t imagine a clinician keeping up with five different logins to make one decision.
Infrastructure Insight: Clinical AI must execute within native systems via HL7, FHIR and DICOM. Without embedded delivery, bidirectional integration and cross-platform connectivity, insights are delayed, fragmented or ignored.
You can’t improve what you can’t measure. For AI to deliver real value, health systems need to know what’s working, for whom and under what conditions. That begins with metrics tied to clear goals, whether it’s reducing treatment delays, improving decisions or speeding up time-to-diagnosis.
An effective measurement strategy spans:
But numbers alone aren’t enough. Real insight comes from understanding how AI functions in daily practice. Are clinicians engaging? Is the AI surfacing meaningful findings? Is it helping — or hindering — workflow?
That’s why we built a unified analytics layer for real-time visibility. Health systems can track adoption, drill into usage by role and tie AI performance to outcomes. These insights support better decisions, stronger training and clearer ROI stories.
Marketplace vendors rarely offer this depth. Without usage tracking or performance validation, it’s hard to improve — or prove — anything. And that’s a barrier to lasting adoption.
Infrastructure Insight: If you can’t track impact, you can’t justify the investment. That’s why measurement is embedded into every aspect of our aiOS™ platform.
Yes, algorithms matter. But they only matter if they’re deployed on top of the right infrastructure.
We support a growing ecosystem of AI: some we’ve built, some from partners and algorithms developed by health systems themselves. However, we don’t offer anything we can’t validate, monitor and support at scale. That’s the difference between a governed platform and an open marketplace.
Marketplaces focus on volume — more tools, more choices — but more doesn’t mean better. If every tool has its own interface, its own integration and no performance oversight, you’re left with complexity, not value.
Scaling clinical AI isn’t about adding more algorithms. It’s about removing friction:
This is what it really takes to run AI across an enterprise. Not just once but everywhere. We built the infrastructure first because we’ve seen what happens when you don’t.
Infrastructure Insight: True scalability requires shared infrastructure. One that can validate, route and monitor every algorithm, regardless of who built it.
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