Retina Scans Won't Save Healthcare. The Data Layer Underneath Them Might.

The boring stuff is where the money is.

That's what I kept thinking while reading CB Insights' interview with Jeremy Stueven, CEO of Vitazi.ai. Vitazi is building what they call a "vendor-neutral orchestration layer for retinal intelligence." In plain English: they're not making another eye scanner or a single AI diagnostic. They're building the plumbing that lets all the existing scanners, PACS systems, and clinical records talk to each other around a patient's longitudinal retinal history.

For anyone who's spent time in healthcare IT, that pitch triggers a specific kind of recognition. It's not glamorous. It's not a flashy algorithm. It's the unsexy infrastructure layer that, if it actually works, makes everything else possible. And based on our data, it's exactly the kind of problem that has practitioners pulling their hair out.

The fragmentation isn't just about eyes.

Stueven frames fragmentation as a retinal-imaging problem. He's not wrong—images trapped in proprietary device silos, manual exports, incomplete records. But our PainSignal dataset tells a much bigger story. We track 1166 problems in Healthcare, and a huge chunk of them are the same disease in different organs: data stuck where it was born, no one able to see the full patient picture.

Take something as basic as blood glucose. We have a problem called MedLog Sync with severity 5/5. It describes nurses having to manually reconcile glucose readings from a glucometer that doesn't talk to the EHR. A device captures a number. A human types it into another system. That human makes an error, or delays, or skips it because they're drowning. The result is a medication decision based on stale or wrong data. That's not a hypothetical—it's a documented pattern across multiple facilities.

The reason this matters for Vitazi's pitch: if you're an investor or a buyer, you might think, "Okay, retinal images are niche. Why should I care?" The answer is that the problem they're solving is not niche at all. It's the same fragmentation that's breaking glucose monitoring, infusion pumps, vital-sign alerts, and a dozen other workflows. The retina is just a smart wedge—a high-value imaging modality with a clear longitudinal story that can anchor a much larger platform play.

The staffing angle no one's talking about.

Here's something Stueven doesn't mention, and it's a glaring omission given the operational reality of healthcare. Fragmentation isn't just a technology problem. It's a workload problem.

We track a problem called NurseFlow Pro with severity 5/5. It describes a new grad nurse responsible for 27+ residents, an unsafe workload that leads to incomplete documentation, missed changes, and clinical errors. When a nurse is under that kind of pressure, the first thing to go is the extra step—the manual data entry, the double-check, the reconciliation. In a world of disconnected devices, every one of those manual steps is a potential failure point.

What does that mean for an infrastructure layer like Vitazi's? Adoption isn't just about integration APIs. It's about whether the solution reduces cognitive load for a burned-out clinician. If a platform asks a nurse to do one more thing—even if that one thing is "verify the data"—it's dead on arrival. The winners in this space will be the ones who make data flow invisibly, so the nurse doesn't have to think about it.

That's a product principle, not just a go-to-market tactic. And it's one that agency developers and builders in this space should internalize: healthcare infrastructure only works if it respects the human at the end of the line.

The quiet land grab for the data layer.

Stueven says the valuable company won't be the one building a single diagnostic algorithm—it'll be the one building the connected data infrastructure. Our data supports that. We track 391 problems in the Data Management category alone. That's not just a lot of gripes. That's evidence of a structural gap.

When you see 391 distinct, documented pain points around data management in healthcare, you're looking at a market that's desperate for a solution. Not a point solution—a layer. The kind of thing that makes all the point solutions stop fighting each other and start sharing.

For seed investors, this is a pattern to recognize: the "picks and shovels" play in clinical data is heating up. Vitazi.ai is one expression of it, starting in ophthalmology. But the same logic applies to oncology, cardiology, lab data, wearables, and more. Whoever builds the clean, vendor-neutral, longitudinal data fabric for a clinical domain will own the relationship with the algorithms that come later. You don't need to bet on which AI diagnostic will win. You need to bet on the road they'll all have to drive on.

A caveat, respectfully.

The TAM numbers in the interview—$600M today, $1.5B by 2034—are plausible but unverifiable from the article. I'd treat them as directional, not gospel. More importantly, the real TAM may be much larger if you consider the broader data orchestration problem. But that larger TAM comes with a much larger competitive set, including established interoperability players, cloud providers, and EHR-adjacent platforms.

Vitazi's edge, per Stueven, is vendor neutrality and a longitudinal, patient-centered architecture. That's a real differentiator if they can execute on the integrations. But integrations are the hard part—the part that kills most healthcare middleware startups. Every proprietary device vendor has a reason to resist opening up. Every health system has a decade of tech debt. The sales cycles are brutal.

So the question for Vitazi isn't whether the vision is right. It's whether they can survive long enough to get enough devices and data sources connected to make the platform sticky before a bigger player copies the playbook.

What this means for builders.

If you're an agency developer or indie hacker in the healthcare space, this interview should reframe how you think about opportunity. Don't build another diagnostic AI. Build the thing that makes the existing diagnostic AIs usable. Build the connector, the synchronizer, the reconciliation layer. The pain is documented, the severity is high, and the market is shouting for it.

We have 592 app ideas in Healthcare on PainSignal, and a huge portion of them are infrastructure-shaped. They're not sexy. They don't trend on Twitter. But they solve problems that nurses, doctors, and administrators feel every single day. And those are the problems people pay to make go away.

The retina is a good place to start. But the real story isn't about eyes. It's about the nerve system of healthcare data—and who's going to build it.