Navigating the AI Lifecycle in GxP - Regulated Industries: Building on the US FDA thinking

As artificial intelligence (AI) continues to play a transformative role in critical sectors like life sciences and healthcare (LSHC), managing the AI lifecycle effectively has become essential for balancing innovation and regulatory compliance. The U.S. Food and Drug Administration (FDA) has recognized this need and is enabling a framework for AI lifecycle management, particularly tailored for Software as a Medical Device (SaMD). This framework will ensure that AI systems are developed, deployed, and monitored in a manner that aligns with safety, compliance, and effectiveness—critical factors in highly regulated industries.

In this article, I will explore the FDA’s expanded AI lifecycle, with some initial thoughts and practical considerations (questions to think and ask) for organizations working in regulated environments. l will try to decipher each phase of the lifecycle, examine the regulatory and operational requirements, and provide my perspective on managing ethical AI systems while fostering continuous improvement.

Understanding the Expanded AI Lifecycle

The FDA’s AI lifecycle management framework encompasses seven distinct but interrelated phases: Planning and Design, Data Collection and Management, Model Building and Tuning, Verification and Validation, Model Deployment, Operations and Monitoring, and Real-World Performance Evaluation. Each phase addresses key technical and procedural actions that are necessary to ensure the robustness and compliance of AI systems.

At the core of the lifecycle is the idea of continuous monitoring and improvement—AI systems must evolve over time, especially in environments where regulatory oversight is stringent, such as in GxP-regulated industries. Furthermore, the FDA’s lifecycle framework integrates overarching considerations of Risk Management and Cybersecurity, ensuring that these systems are safe, secure, and compliant from inception to deployment and beyond.

Phase 1: Planning and Design

The Planning and Design phase lays the foundation for the entire AI lifecycle. In this phase, organizations define the problem space, identify data sources, and establish a framework for ensuring data quality and ethical AI practices, including fairness and bias mitigation. This phase also involves the critical decisions around algorithm selection and model design.

From a compliance perspective, one of the most important considerations during the Planning and Design phase is ensuring that the AI system’s design aligns with regulatory requirements, particularly those related to auditability and explainability. In regulated industries, AI systems must be interpretable, meaning the reasoning behind their decisions must be transparent and understandable. This is crucial for meeting FDA and GxP requirements, where regulatory bodies demand full traceability of decision-making processes.

Questions to ask:

• Problem Definition: What is the intended purpose of the AI system? How will it benefit users or patients?
• Ethics and Fairness: Have you addressed potential biases in the data and model design? Are fairness and transparency prioritized?
• Scalability and Infrastructure: Is the system designed to scale while maintaining performance and compliance with regulations?

This phase also involves planning for data quality assurance, observability, and ensuring that the AI system’s deployment is integrated seamlessly with existing infrastructures.

Phase 2: Data Collection and Management

Data is the foundation of AI, and in regulated environments, data governance, privacy, and integrity are paramount. The Data Collection and Management phase focuses on ensuring that the data used to train, validate, and update AI systems is suitable and of high quality. The FDA emphasizes the importance of data traceability and version control, ensuring that all data modifications can be tracked and audited.

In industries like life sciences and healthcare, ensuring data privacy and security is not just a best practice—it is a regulatory mandate. Sensitive information such as patient data must be handled with extreme care to avoid breaches and ensure compliance with privacy laws like HIPAA (Health Insurance Portability and Accountability Act).

Questions to ask:

• Data Suitability: Is the data relevant and appropriate for the model being developed? Are there any gaps in data quality or bias?
• Data Governance and Documentation: Are processes in place for maintaining data documentation, access control, and audit trails?
• Bias Mitigation: Is the data representative of the real-world environment? Have strategies been implemented to mitigate potential biases?

Data labeling, annotation, and sampling must also be conducted with accuracy and precision, as errors in this phase could propagate throughout the lifecycle and affect model performance.

Phase 3: Model Building and Tuning

The Model Building and Tuning phase involves the technical work of selecting and fine-tuning AI models to achieve the desired performance outcomes. This includes tasks such as hyperparameter tuning, feature engineering, and cross-validation. However, the process must be conducted with both performance and regulatory compliance in mind.

In regulated industries, models must not only perform well but also be interpretable. Complex models such as deep learning architectures may offer higher accuracy but are often difficult to interpret, which could pose challenges during regulatory inspections or audits. To mitigate these issues, organizations must find the right balance between model complexity and explainability.

Questions to ask:

• Model Transparency: Is the model interpretable? Can the decision-making process be audited and explained to non-technical stakeholders?
• Robustness and Generalization: How well does the model generalize across different datasets and environments? Have you implemented robustness training to prevent bias or overfitting?
• Validation and Metrics: Are the validation metrics aligned with both performance goals and regulatory requirements?

The FDA’s emphasis on robustness and generalization in model building ensures that AI systems can handle real-world variability without compromising safety or effectiveness.

Phase 4: Verification and Validation

The Verification and Validation phase ensures that the AI system performs as intended and meets regulatory expectations. It includes comprehensive model evaluation metrics, deployment testing, and error analysis. This phase is essential for ensuring that the system is both compliant and effective before it is put into production.

In regulated environments, continuous validation is critical. AI systems, especially those in healthcare or pharmaceuticals, must be regularly monitored and validated to ensure that they continue to meet performance and safety standards over time. This aligns with the FDA’s focus on post-deployment monitoring.

Questions to ask:

• Validation Strategies: Have you designed a comprehensive validation strategy that aligns with both operational goals and regulatory requirements?
• Model Comparison and Error Analysis: How does the AI system compare to previous models or baseline systems? What error patterns have been identified, and how can they be mitigated?
• Documentation and Reporting: Are all verification and validation activities fully documented for regulatory audits?

Phase 5: Model Deployment

In the Model Deployment phase, organizations must ensure that the AI system integrates seamlessly into their existing infrastructure while maintaining compliance. This phase covers the scalability, reliability, and performance monitoring aspects of deployment.

A key challenge in regulated industries is managing version control. AI systems often evolve over time, with new versions being deployed to address performance issues or incorporate new data. Ensuring that every version is properly documented and auditable is crucial for maintaining compliance with regulations like 21 CFR Part 11.

Questions to ask:

• Versioning and Monitoring: Is there a system in place for tracking and monitoring all AI model versions? Are logs maintained for regulatory audits?
• Scalability and Performance: Does the AI system maintain its performance when scaled to production environments? Are monitoring tools in place to track real-time performance?

This phase also emphasizes the need for rigorous governance, ensuring that all compliance controls are in place and that the system can handle operational challenges.

Phase 6: Operations and Monitoring

Once the AI system is deployed, the focus shifts to Operations and Monitoring. This involves real-time performance monitoring, alerting mechanisms, and feedback loops to ensure that the AI system continues to meet both operational and regulatory standards.

Regulatory requirements demand continuous logging and auditing to ensure that the AI system is functioning as intended. Any deviations or model drift must be promptly addressed through incident response mechanisms.

Questions to ask:

• Real-Time Monitoring: Are real-time monitoring and alerting systems in place to detect performance issues or model drift?
• Logging and Auditing: Are logs maintained in a way that ensures full traceability of all AI system activities?
• Continuous Improvement: Are there feedback mechanisms in place to ensure continuous improvement of the AI system based on real-world data?

Phase 7: Real-World Performance Evaluation

The final phase of the AI lifecycle is Real-World Performance Evaluation, where the AI system is assessed against Key Performance Indicators (KPIs). This phase ensures that the system remains effective in real-world environments and continues to meet compliance requirements.

Error analysis, feedback collection, and continuous reporting are essential components of this phase, ensuring that the AI system adapts to evolving real-world data.

Questions to ask:

• Drift Detection: Is the AI system evaluated for drift over time? How does it perform compared to initial baseline metrics?
• Error Analysis and Feedback: How are errors tracked and mitigated? Is there a system for collecting feedback from users or operators?
• Documentation and Reporting: Are real-world performance evaluations fully documented and auditable?

Overarching Considerations: Risk Management and Cybersecurity

Two considerations span the entire AI lifecycle: Risk Management and Cybersecurity. These factors are essential for ensuring that AI systems remain safe, secure, and compliant throughout their lifecycle.

Effective risk management strategies involve identifying potential risks at each phase of the lifecycle and implementing controls to mitigate them. Cybersecurity, on the other hand, focuses on protecting sensitive data and preventing breaches that could compromise both safety and compliance.

In conclusion

The FDA’s expanded AI lifecycle management framework offers a comprehensive approach to managing AI systems in regulated environments. Each phase provides unique considerations for ensuring compliance, operational integrity, and ethical AI practices.

Organizations must adopt a lifecycle-based approach to ensure that their AI systems not only meet regulatory requirements but also continue to evolve and improve over time. By prioritizing data integrity, model transparency, and real-world performance monitoring, organizations can successfully navigate the complexities of AI in regulated industries.

References

1. US FDA Blog: A Lifecycle Management Approach toward Delivering Safe, Effective AI-enabled Health Care
2. US FDA AI/ML-Based Software as a Medical Device (SaMD) Action Plan. 2021
3. US FDA Digital Health Innovation Action Plan. 2017
4. US FDA Guidance. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices. 2017.
5. Good Machine Learning Practice (GMLP) Guiding Principles. U.S. Food and Drug Administration, Health Canada, and UK MHRA. 2021.
6. 21 CFR Part 11. Electronic Records; Electronic Signature
7. US FDA Artificial Intelligence and Machine Learning Software as a Medical Device Action Plan. 2021.
8. Annex 11: Computerised Systems. European Commission
9. ISO 14971: Medical Devices – Application of Risk Management to Medical Devices
10. NIST Cybersecurity Framework. National Institute of Standards and Technology

Disclaimer: The article is the author's point of view on the subject based on his understanding and interpretation of the regulations and their application. Do note that AI has been leveraged for the article's first draft to build an initial story covering the points provided by the author. Post that, the author has reviewed, updated, and appended to ensure accuracy and completeness to the best of his ability. Please use this after reviewing it for the intended purpose. It is free for use by anyone till the author is credited for the piece of work.