(Governing the Algorithm — Intro)
A Different Kind of Compliance Problem
Every regulated product eventually reaches a point where compliance stops being about the launch and starts being about the operation. Pharma has pharmacovigilance. Medical devices have post-market surveillance. Healthcare AI is now arriving at its own version of this shift — except the thing being governed doesn't stay still.
A drug's formula doesn't change after approval. A traditional device's hardware doesn't change after clearance. But an AI model can drift, get retrained, encounter populations it never saw in validation, and behave differently a year into deployment than it did on day one.
"We're cleared" answers what the product was allowed to do at one point in time.
"We're governed" answers whether it can still be trusted today.
Those are not the same claim, and increasingly, hospitals, investors, and regulators are asking for the second one specifically.
Why This Is Emerging Now
Three forces are converging to push AI governance from "nice to have" to "expected":
✔ Regulatory evolution — the FDA's Predetermined Change Control Plan pathway now explicitly requires vendors to describe how their AI is allowed to change, which is a governance question, not just a technical one
✔ Enterprise risk appetite — hospital IT and compliance teams have watched enough AI incidents in the news to start asking about oversight and accountability before they ask about accuracy
✔ International standards catching up — frameworks like ISO/IEC 42001 for AI management systems are giving buyers a formal way to evaluate governance maturity, the same way ISO 13485 became a baseline expectation for device quality systems
Companies that treat this as a future problem are going to find it's already showing up in today's procurement conversations.
What "Governance" Actually Means Here
This series will use governance in a specific, practical sense — not as a compliance buzzword, but as the operational system that answers five recurring questions:
- Data Governance
— Where did the training and monitoring data come from, and can that be trusted?
- Model Risk Management
— What could this model get wrong, and how would anyone know?
- Human Oversight
— Where does a human actually intervene, and can they meaningfully do so?
- Change Control
— What's allowed to change about this model after deployment, and how is that validated?
- Accountability
— When something goes wrong, who is actually responsible, and how was that decided in advance?
Every article in this series will dig into one piece of this system — not as regulatory theory, but as decisions founders and executives need to make well before an auditor, hospital, or incident forces the issue.
Who This Series Is For
This is written for the same audience as Beyond FDA: founders and executives at non-U.S. Healthcare AI companies who've already done the hard work of building a clinically capable product, and now need the operational infrastructure that lets hospitals, investors, and regulators trust it long after launch day.
If Beyond FDA was about earning the right to enter the U.S. market, Governing the Algorithm is about earning the right to stay in it.
Final Thought
Clearance is a moment.
Governance is a system.
A cleared model proves it worked once, under specific conditions.
A governed model proves it can be trusted continuously, as conditions change.
The companies that build this system early won't just pass audits more easily — they'll be the ones hospitals default to trusting when the next AI incident makes headlines and everyone else has to scramble to prove they were never the problem.
Next in the Governing the Algorithm Series:
Why "It's FDA Cleared" Is Not the Same As "It's Governed"
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