Navigating AI Compliance in Life Sciences: FDA Policy Insights for AI Governance

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Summary
Join us for a conversation on artificial intelligence in life sciences, featuring Danielle Dorfman Faruq, Digital Health and AI/ML Policy Specialist at the FDA's Digital Health Center of Excellence, and Renee VonBergen, Vice President of Data and AI at MasterControl.
As AI-enabled technologies rapidly transform quality management and regulatory processes, life science organizations face critical questions about implementing these tools while maintaining FDA compliance. This webinar bridges regulatory guidance with practical application, giving quality professionals the insights they need to confidently adopt AI in their operations.
You'll Learn:
-FDA's Perspective on AI Governance Danielle will share the FDA's approach to five critical pillars of AI implementation:
- Data Quality and Management: Understanding data integrity requirements for AI systems
- Human Oversight: The role of human supervision in AI-assisted decision-making
- Transparency: Explainability and documentation expectations for AI algorithms
- Ongoing Monitoring: Post-deployment surveillance and performance tracking requirements
- Governance: Establishing robust AI governance frameworks that align with regulatory expectations, including Good Machine Learning Practices (GMLP)
-MasterControl's Embedded AI Approach
Renee will share how MasterControl has designed AI foundationally with compliance built in:
- How MasterControl's AI approach addresses each of the five critical pillars
- Pre-built compliance controls and safeguards embedded in the platform
- Behind-the-scenes architecture that ensures data quality, traceability, and auditability
- What MasterControl manages versus what requires customer action Your Compliance Roadmap
- Documentation requirements for AI system validation and use
- Implementing human-in-the-loop processes effectively
- Establishing change controls for AI systems
- Ongoing monitoring and performance evaluation protocols
- Building internal governance structures for AI adoption




