There’s a silent revolution underway in the way we regulate Artificial Intelligence in medicine.
For years, the conversation has been all about validation: collecting data, measuring performance, defining protocols, proving that the model works.
The logic was linear: if it works, it can be certified.
But 2026 marks a turning point.
It will no longer be enough to show that an algorithm works — we’ll need to show that it will keep working over time, in real-world contexts, across diverse patients, data, and conditions.
AI models are no longer static systems. They’re dynamic, adaptive, constantly learning from new data.
Which means the regulatory question is shifting: from “Is the model valid now?” to “Can we govern its evolution?”
From validation to governance.
This is not a theoretical shift. It’s already written into the upcoming regulatory frameworks — the EU AI Act, the EMA guidance on AI use, and the FDA’s Predetermined Change Control Plan (PCCP).
All are converging on one message: compliance is no longer a one-off event, but a continuous process that must follow the algorithm through its entire lifecycle.
Until recently, software was treated like a finished product — validated once, fixed parameters, released as a stable version.
That no longer applies to adaptive, self-updating machine learning models. These can improve — but they can also drift. And what you can’t govern, you can’t control.
This raises new, practical questions regulators are already addressing:
- How do we guarantee safety when each update changes the model’s behavior?
- How do we distinguish real improvements in clinical outcomes from statistical noise?
- How do we update regulatory documentation when the model evolves?
- And most importantly, how do we monitor risks and performance once the algorithm is deployed in real life — beyond the controlled space of a trial?
The message from regulators is clear, and logical:
It’s not about how innovative your AI is, but how responsibly you can govern it over time.
The FDA has already formalized this through the PCCP, requiring companies not to “react” to change but to design for it – before approval, before commercialization.
That means defining in advance which parts of the model may evolve, how they will be monitored, and how safety will be preserved through each iteration.
Change is no longer a risk; it’s part of the design.
Europe is moving in the same direction. Under the AI Act, high-risk systems (including AI-enabled medical devices and clinical decision-support software) must be explainable, traceable, and continuously monitored.
The EMA goes even further: AI must not only work; it must be understandable, controllable, and justifiable.
What does this mean for sponsors, startups, and medtech companies?
It means that validating an algorithm is just the beginning.
Governing it is the real responsibility — and the true competitive edge.
That requires:
- Data governance that prevents drift and bias
- Quality systems where updates are built into the lifecycle
- Evidence generation that includes real-world data, not just clinical protocols
- Continuous post-market monitoring, integrated from day one
This is where the role of clinical research organizations is evolving, too.
Not CROs that simply “run studies”, but CROs that accompany algorithms over time: helping sponsors build not only evidence, but governance.
At We4 Clinical Research, we don’t see an algorithm as a static product.
We see it as a living organism — something that learns, adapts, and grows.
And because of that, it needs to be accompanied, not just certified.
If you’re developing or managing an AI-enabled device, this is not a future concern.
It’s the question that will define your competitiveness in the years to come.
Validation isn’t enough.
Governance is the next frontier.