FDA Insights for AI Compliance in Life Sciences

What 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