What Pharma Can Learn from MedTech's AI Validation Playbook

The Shift Pharma Needs to Make

One of the biggest differences I've seen between pharma and MedTech is how we approach validation.

MedTech forced us to solve a problem that pharma is only now confronting: how do you validate a system whose behaviour is shaped by data rather than deterministic code?

The answer lies in a fundamental conceptual shift. Traditional software validation asks whether the system works as specified. In MedTech, we added a second question: does the system support the right decisions in real-world conditions?

That shift, from product validation to decision-impact validation, is exactly what pharma needs to adopt.

In practice, I often see the same distinction emerge:

• Verification asks "did we build it right?" and focuses on performance, robustness, bias, and cybersecurity.
• Validation asks "did we build the right thing?" and ensures the AI system is fit for its intended GxP decision-support role, often including human-in-the-loop oversight.

In pharma terms, verification aligns with installation and functional testing, while validation aligns with fitness for intended use or user acceptance testing. The concepts themselves are not new, but their application to AI is.

Regulation as an Enabler, Not a Brake

Regulation is often perceived as a barrier, but frameworks such as GxP, GAMP, ISPE, and the EU AI Act provide something essential: clarity.

They require organizations to define:

• Intended use
• Acceptable risk
• Data governance
• Human oversight
• Monitoring
• Traceability

In doing so, they transform experimentation into trustworthy, scalable systems and provide guardrails for AI design.

In MedTech, Notified Bodies already expect additional controls around data governance and AI when it is part of a medical device.

At the same time, a broader standards ecosystem is maturing. ISO/IEC 42001, GAMP 5, Annex 22, and the ISPE AI maturity model are converging into a framework that pharma can adopt rather than build from scratch.

Start with What Is Proven

If I would give one practical recommendation, it would be this: start with what is already proven to work. A pragmatic starting point for pharma is to begin with static, deterministic AI models under human oversight.

These models offer:

• Predictability
• Control
• Straightforward GMP compliance

At the same time, they help build internal trust and maturity, paving the way for more advanced AI systems.

This mirrors MedTech, where static models remain the standard for the most critical applications.

And importantly, organizations do not have to tackle this alone. By combining QbD Group's compliance and validation expertise with delaware's digital and implementation capabilities, both the regulatory and technical dimensions can be addressed from the outset.

Looking to Go Deeper?

Understanding AI validation in a GxP context requires both regulatory insight and practical implementation guidance.

Watch the on-demand webinar on AI in Life Sciences to explore real-world examples and practical frameworks.

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