Skip to main content
As the pharmaceutical and life sciences industries continue to explore the benefits of artificial intelligence (AI), it is critical to apply the same rigor and structure to AI implementations as we would to any other computerized system operating in a GxP-regulated environment. While AI offers new efficiencies and insights, it also introduces new risks—particularly when applied to decision-making processes that impact product quality, patient safety, or data integrity.
To ensure AI applications are fit for purpose and compliant with regulatory expectations, validation must begin with a deep understanding of how the application works, the data it depends on, and how its performance will be measured and maintained.
1. Understand the Application and Its Intended Use
Validation starts with a clear definition of the AI application’s purpose. Whether it is used to detect anomalies, automate document classification, or support predictive maintenance, the intended use must be explicitly defined. This aligns with FDA expectations that systems are validated for their specific, intended purpose—not just generally “tested.”
Documentation should detail the scope of functionality, the business process it supports, and what decisions (if any) it influences. This step ensures that validation activities are relevant and risk-based, and that stakeholders fully understand how the application fits into the larger GxP environment.
2. Understand the Data the AI Application Uses
AI models are only as reliable as the data used to train and operate them. Validation must account for data sources, data integrity, and the potential for bias or drift over time. For supervised learning models, questions like “Was the training data representative of real-world conditions?” and “How was the data cleaned, labeled, and versioned?” become part of the validation narrative.
In a GxP context, traceability is paramount. This includes not only where the data comes from, but how it is stored, protected, and maintained in alignment with ALCOA+ principles.
3. Assess the Risk Associated with the AI Application
The level of validation required should correlate with the risk the AI application introduces. Risk assessments must consider:
- Impact on patient safety
- Potential for data integrity issues
- Whether AI outputs inform or replace human decision-making
In many cases, AI is used in a supportive role. Even then, outputs may shape critical decisions, meaning appropriate controls and reviews must be established. If the AI application is involved in any part of the product lifecycle—from development to release—it should be treated with heightened scrutiny under validation and quality system procedures.
4. Recognize That AI Is a Mathematical Model, Not Magic
AI is fundamentally a set of mathematical instructions and statistical probabilities. Unlike rule-based systems, AI operates in a probabilistic manner, and its performance can vary based on inputs and constraints. Understanding the model type (e.g., regression, neural network, decision tree), its parameters, and the constraints applied during development is necessary to define the model’s boundaries and limitations.
In a regulatory context, this means defining what constitutes acceptable performance, identifying known failure modes, and ensuring users understand the limitations of the system. Validation efforts should challenge the model under realistic and edge-case conditions to confirm robustness.
5. Plan for Ongoing Monitoring and Maintenance
Validation of AI is not a one-time event. Continuous performance monitoring is essential, especially as the operating environment or input data changes. This may include:
- Periodic re-validation or re-training
- Monitoring for model drift or performance degradation
- Implementing alert mechanisms for unexpected outcomes
Regulators are increasingly focused on lifecycle management for AI systems, which includes maintaining documentation of version changes, updates to algorithms, and controls around re-training processes.
A successful validation plan includes a strategy for ongoing control—ensuring that the AI continues to operate within defined boundaries and continues to fulfill its intended use over time.
Conclusion
AI has the potential to transform regulated industries, but only when implemented with discipline, transparency, and alignment to GxP principles. For QA, validation, and IT professionals, the first step is understanding how AI works—and then applying the right level of control to ensure it delivers reliable, compliant, and traceable outcomes.
At Performance Validation, we are committed to helping our clients navigate the evolving landscape of AI and advanced digital solutions with a validation-first mindset—grounded in quality, risk management, and regulatory expertise.
