AI, Lasers, and Lessons We Still Haven’t Learned
- Catherine Del Vecchio Fitz
- Jun 12, 2025
- 4 min read
Why the latest “miracle” diagnostic should make us pause—and how we help teams build what their story actually promises.
TL;DR
Health tech is still repeating old mistakes—this time with lasers and AI. In diagnostics, real-world evidence, and early cancer detection, the pressure to tell a big story often outpaces the systems needed to support it. This post unpacks that gap, why it keeps happening, and how Savvyn Insights helps life sciences teams build structures that hold up under scrutiny. If your product, data, or strategy feels a little misaligned, you’re not alone—and you’re exactly who we work with.
You Lost Me at Lasers
Recently, headlines broke about Haemanthus, a diagnostics startup founded by the partner of Elizabeth Holmes. The company claims to use Raman spectroscopy to scan blood, saliva, urine, and sweat with a laser-powered, AI-guided device.
The pitch? Instant diagnostics. Stamp-sized wearables. Starting with pets. Scaling to humans.
You lost me at lasers.
To be fair, the underlying science isn’t necessarily flawed. Raman spectroscopy is real. AI has enormous potential. But what’s concerning is the pattern: a technically plausible premise wrapped in slick marketing and premature promises. It’s dressed in smarter branding than Theranos, but it runs the same risk—building momentum before the foundation is in place.
This isn’t just about one company. It’s a case study in how hype spreads faster than infrastructure.
When the Narrative Outruns the Science
Diagnostics—especially LDTs—can move quickly. With no requirement for FDA approval before going to market, teams can launch based on internal validation alone. That flexibility is valuable. But it also places a heavier burden on internal systems to self-regulate.
And that’s where cracks tend to form.
It often starts with a simple narrative meant to explain the vision. That narrative becomes a slide, the slide becomes a pitch, and the pitch becomes embedded in how the company sees itself. At some point, the story hardens into dogma—and when the science starts to evolve, the story doesn’t.
This drift isn’t always obvious. It shows up as confusion between teams, mismatched claims across departments, or surprise during diligence when numbers can’t be easily explained.
The risk isn’t just external. It’s internal misalignment that leads to inefficiency, delay, or worse—accidental misrepresentation.
“Let the Data Speak”—Unless It Can’t
At Savvyn Insights, we believe data should speak for itself. But that only works when it’s well-structured, interpreted accurately, and framed with integrity.
The problem isn’t always a lack of data. Sometimes it exists—but it’s been massaged, reframed, or selectively filtered to fit the story rather than guide it. That’s not unusual. It’s what happens under pressure: to raise a round, launch a product, or justify a partnership.
That’s why systems matter. If your infrastructure can’t support the story, you’re not just risking your message—you’re eroding trust from the inside out.
Diagnostics Aren’t the Only Gray Zone
The same pressures show up across adjacent domains—especially where standards are evolving and commercial pressure is high.
Real-world evidence often gets shaped to reinforce commercial claims. Subgroups are spotlighted. Endpoints are reframed. Exploratory insights masquerade as predictive value.
Clinical utility studies can suffer similar erosion. Inclusion criteria loosen. Endpoints soften. Outputs are curated to highlight what’s favorable, not what’s representative.
Scientific communications feel the squeeze too. Posters and abstracts emphasize best-case scenarios over real-world context—because “showing traction” becomes the goal.
None of this is unique. But it’s a sign that the internal scaffolding isn’t keeping pace with external demands.
Selling the Vision vs. Building the Reality
Every early-stage company faces this paradox: you need a great story to raise capital, but you need capital to build the thing you’re promising.
So teams sell the future—and figure out the rest later.
To an extent, that’s expected. But the danger comes when the “later” never arrives. When systems fall behind. When teams keep selling past the point of operational readiness.
That’s not just risky. It’s inefficient. And it often leads to burn, confusion, or loss of credibility when things inevitably need to be recalibrated.
The Clean Data Advantage
The real race in diagnostics—and life sciences more broadly—isn’t about who sounds the most advanced. It’s about who can back their story with substance.
Clean data isn’t just tidy spreadsheets. It’s clarity in what you're measuring, consistency in how you're tracking it, and confidence that your numbers hold up under pressure. It’s the difference between chasing traction and actually having it.
From where I sit, the real winners are the ones with:
Cross-functional alignment on key metrics
Clear definitions and version control across datasets
Dashboards that tell the same story your decks do
Systems that surface real insights—not just repackage assumptions
And increasingly, this isn’t just a nice-to-have—it’s what stakeholders expect. Decision-makers want to trace where the numbers come from. Investors want to understand what’s real versus what’s aspirational. Partners want to know the foundations are solid. And regulators? They want proof that the system works, not just that the science does.
The companies that succeed won’t just have great science. They’ll have the operational backbone to turn it into something scalable, repeatable, and real.
That’s not a comms problem. It’s a systems problem.
And it’s exactly what we help solve.
Final Thought
The next Theranos won’t look like Theranos. It will speak fluent AI. It will be smarter, slicker, and funded. And it will unravel—quietly, then suddenly—if no one asks: Does this actually work?
If your team is balancing bold science with messy operations—you’re not alone. Let’s fix that.
Thanks for reading,
—Savvyn (your partner in ruthless efficiency)
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