What separates an AI strategy that scales from one that stalls?
It’s a question more health systems are asking as the gap grows between initial projects and enterprise outcomes. One source of confusion: The difference between a clinical AI platform and an AI marketplace, two terms that sound similar but function differently in practice.
In this Q&A, Reut Yalon, PhD, Chief Product Officer at Aidoc, shares what a true platform requires, why marketplaces often fall short and how health systems can evaluate when vendors claim they have a “platform.”
RY: A clinical AI platform is an end-to-end, integrated system. It doesn’t just surface outputs; it embeds AI directly into clinical workflows, delivering insights in real-time, where and when they’re needed. It also ensures that algorithms run natively within the infrastructure, continuously analyzing data at scale. Just as important, the impact is measurable through internal tools to track performance and support ongoing optimization across clinical and operational outcomes.
This kind of structure addresses the three biggest barriers to clinical AI adoption: disconnected data, clinician pushback and unclear ROI. Without a unified infrastructure, even the best algorithms struggle to gain traction.
Marketplaces, on the other hand, function more like app stores. Each tool has a different interface, a different support system and its own way of handling data. That might work on your phone, but in healthcare — where workflows are tightly regulated and difficult to change — every added tool introduces friction.
Radiology is a good example. Radiologists work in a highly structured environment with PACS, a unified worklist and strict workflows. They can’t toggle between tools with different interfaces, alert systems or data flows. That’s why integration at the platform level is so critical. This is true not just within radiology, but across care teams. For instance, our radiology desktop app connects directly to our mobile app, allowing radiologists to trigger workflows and send real-time notifications to downstream clinicians without leaving their system.
A true platform has to do more than offer algorithms. It needs a scalable, accurate and intelligent way to run AI — one that integrates into clinical systems, drives action and measures impact. Without that foundation, AI just adds complexity. And complexity doesn’t scale.
RY: A lot of what’s being called a “platform” today simply isn’t one. In most cases, it’s a collection of standalone tools marketed under a single brand. There’s no unified workflow, no consistent integration and no way to monitor impact. Calling it a platform sounds more scalable, so the term gets stretched.
A true platform has four foundational layers:
First, a way to run AI. That means ingesting and normalizing data — from imaging, EHR and other systems — and orchestrating the logic that determines which AI to run, on what data and when. Just as important, there must be a way to monitor performance over time to ensure algorithms continue to operate accurately and consistently. Most vendors don’t have this infrastructure. They rely on health systems to piece it together.
Second, a way to drive action. AI only matters if it fits into the way clinicians already work. That’s why we’ve invested heavily in workflow with our platform, the aiOS™ — desktop, mobile, PACS and EHR integrations — so insights show up in the right place, at the right time, without disrupting clinical routines.
Third, the ability to measure impact. Health systems need visibility into how AI is being used, what it’s changing and where it’s delivering value. Our customers get full transparency, including engagement metrics, performance insights and downstream clinical outcomes.
Finally, the clinical use cases themselves — the solutions designed to support specialties like neuro, vascular, radiology and more. Without the right foundation in place, even the best solutions won’t make an impact. They become difficult to deploy, harder to use and nearly impossible to scale.
The word “platform” signals scale, stability and strategic value. However, without the technical backbone to match, it’s a platform in name only, and that mislabeling puts health systems at risk of overcommitting to something that can’t deliver.
RY: It often happens in systems that haven’t deployed AI yet. They see a marketplace offering 100 algorithms and think, “That’s what we need since we’ll need AI for everything.” On paper, it looks like a shortcut to scale.
What they don’t realize until it’s too late, is the operational burden that comes with that choice. Every vendor requires its own legal review, security risk assessment, integration work and workflow alignment. We’ve seen health systems spend months negotiating a single marketplace deployment, only for clinicians to reject it because it didn’t show up in their workflow.
What they don’t realize is the operational cost of deploying that many tools. IT teams burn time. Clinical champions lose trust. The moment AI becomes a burden instead of a benefit, adoption stalls.
On top of that, marketplaces lack consistency. One result shows up in PACS, another in a browser and another in a mobile app. There’s no unified way to track what’s working. Health systems we work with don’t ask us about marketplaces because once they’ve seen what it takes to scale AI in practice, they understand the difference.
What happens when a health system chooses a marketplace approach?
RY: They realize that volume isn’t the same as value. Yes, AI can bring value across service lines but only if it’s implemented in a scalable way. Most marketplaces simply don’t have the infrastructure to support enterprise deployment, and not all algorithms are equal. Some vendors are unproven or lack performance data. Others aren’t clinically validated at all.
At Aidoc, we won’t offer a use case — whether it’s ours or a partner’s — unless we can verify its clinical value. We embed every solution into our platform, ensure it works within the workflow and monitor it like it’s our own.
RY: Health systems have become more sophisticated in how they evaluate AI. Today, organizations are asking much tougher questions, and they’re looking for proof, not just promises.
They can start by looking at the contracting structure. Are they signing separate agreements and running individual security reviews for every use case? That’s a telltale sign of a marketplace.
Next, evaluate the integration lift. Can a single integration support multiple applications across clinical domains? If not, it’s not scalable. They should also dig into how AI is actually run: What data is being consumed by which solution? How are data logics applied consistently and scalably across use cases? And how does the vendor ensure performance is accurate — not just at go-live, but over time, as real-world conditions change?
Don’t just look at what’s possible, look at what’s already live. If a vendor can’t show multiple clinical use cases running today within a single health system, that’s a red flag.
Then ask about workflow integration. Is the experience siloed per solution, or unified across the clinical domain? More importantly, is it connected across different users — for example, can radiologists seamlessly trigger downstream actions for care teams? If the answer is no, it won’t work in real-world environments.
Finally, ask about transparency. How do you monitor usage? How do you measure impact? Can you track outcomes across applications? If there’s no clear answer, the platform claim doesn’t hold up.
Some organizations even follow frameworks, like the American Hospital Association’s (AHA) Health Care AI Principles, to guide internal vetting. Still, the core question is always the same: Can this platform scale across our health system without creating complexity?
What about health systems that want flexibility, who like the idea of choosing between vendors for the same use case?
RY: We understand that, but flexibility only matters if it’s operational. Most marketplaces can’t evaluate how different vendors perform on your data. They don’t have the ground truth, the analytics frameworks or the time to monitor performance in a clinically rigorous way.
We validate every solution, whether we built it or not. If we offer it, it works and it’s integrated. That takes more time, but it ensures you’re scaling responsibly.
Over the next few years, we plan to open our platform to more vendors, both for additional use cases and, where appropriate, multiple vendors for the same use case. Still, we’ll do it the smart way, with the infrastructure, orchestration and monitoring tools in place to ensure every solution meets the same standard of integration, performance and impact.
What separates AI success stories from the rest in the next five years?
RY: Real-world deployment. It’s one thing to say you have 100 algorithms. It’s another to show 20 running in a single health system with validated clinical impact, integrated workflows and clear ROI.
That’s what will define success: outcomes, not algorithms. So far, I haven’t seen a marketplace that’s proven it can deliver that at scale.
