Nick Armstrong, Senior Director of Digital Enablement at CAI and co-chair of the ISPE Community of Practice on AI, specializes in the intersection of artificial intelligence and regulated industries. With a passion for driving innovation while maintaining compliance, Nick brings valuable insights into how cutting-edge technologies can transform pharmaceutical processes. In the following blog, Nick delves into the FDA’s new framework for AI credibility, providing a thoughtful analysis of its implications for building trust in AI and ensuring its responsible adoption in the pharmaceutical industry.
In the pharmaceutical industry, we’ve grown accustomed to viewing compliance as a series of well-defined checkpoints. But when it comes to artificial intelligence (AI), regulatory requirements are less about “one-and-done” validations and more about continuous vigilance. AI models are dynamic by nature—they adapt, evolve, and respond to new data inputs. This inherent flexibility is one of AI’s greatest strengths, but it also introduces unique risks. How do we ensure that an AI model remains reliable and trustworthy throughout its entire lifecycle?
The FDA’s latest guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, provides a compelling answer: life cycle maintenance. For those of us working at the intersection of AI and pharmaceutical manufacturing, this concept is both a challenge and an opportunity. It challenges us to rethink traditional approaches to quality management while providing a structured roadmap for monitoring and sustaining AI model performance.
AI Models Are Living Systems
AI models don’t operate in isolation. They’re deeply integrated into complex systems—whether predicting patient outcomes, optimizing manufacturing processes, or monitoring post-market safety signals. Over time, the environments in which these models operate inevitably change. Data shifts, operational processes evolve, and new variables come into play. Without a proactive maintenance strategy, an AI model trained on yesterday’s data may fail to perform accurately in today’s world.
The FDA emphasizes this point, noting that AI models are highly sensitive to “data drift”—changes in input data that differ from the data used during training. For example, in pharmaceutical manufacturing, variations in raw material characteristics or process conditions could impact an AI model’s ability to detect deviations or anomalies. To address this, the guidance calls for sponsors to adopt a risk-based approach to life cycle maintenance, ensuring that AI models are continually assessed and updated to remain fit for their intended use.
Key Components of Life Cycle Maintenance
The FDA outlines several core principles for effective life cycle maintenance, and as an AI strategy consultant, I find these principles resonate with the industry’s growing need for adaptability and resilience.
First, performance monitoring is critical. Sponsors must define performance metrics, monitor them regularly, and establish thresholds for action. For high-risk applications, such as AI systems used to detect manufacturing defects, the monitoring process may need to be both rigorous and frequent.
Second, the guidance stresses the importance of change management. AI models may evolve due to intentional updates (like retraining on new data) or model-directed changes (where the model adapts autonomously). Sponsors must assess the impact of these changes and, if necessary, re-execute credibility assessment steps to validate performance under the updated conditions.
Finally, the FDA encourages sponsors to integrate life cycle maintenance plans into their broader pharmaceutical quality systems. By embedding AI oversight into established frameworks for quality management, organizations can streamline compliance while maintaining the flexibility to innovate.
Rethinking AI Oversight
What I find particularly striking is how the FDA’s guidance balances rigor with pragmatism. It acknowledges that not all AI applications carry the same level of risk. For lower-risk models, oversight can be relatively light-touch, focusing on periodic performance checks. But for higher-risk applications, such as those with significant safety implications, the maintenance strategy must be more detailed and robust. This tailored, risk-based approach ensures that resources are directed where they’re needed most.
Why It Matters
Life cycle maintenance isn’t just a regulatory requirement—it’s a safeguard for trust. The pharmaceutical industry is built on the promise of safety, efficacy, and quality. If we want AI to become a cornerstone of drug development and manufacturing, we must uphold these principles at every stage of the model’s lifecycle. The FDA’s guidance provides a framework for doing just that, enabling organizations to innovate responsibly.
As we move forward, life cycle maintenance will be a defining feature of successful AI implementations in pharma. It’s not just about meeting compliance standards; it’s about building systems that adapt to change, mitigate risk, and ultimately improve outcomes for patients. For AI to thrive in this industry, it must prove its reliability—not just once, but over time.
Related Links
- Part 1: A Framework for Trust: Understanding the FDA’s AI Risk-Based Credibility Assessment
- Learn how the FDA’s framework builds trust in AI for pharma.