Full course coming soon

Lifecycle Evidence for Regulated AI Products

7 chapters · 32 lessons

1. 1. Intended Use and Risk Classification

   4 lessons

   Learn to define what your AI system is allowed to do and how that scope determines regulatory pathway and evidence burden.

2. 2. Premarket Validation Study Design

   5 lessons

   Design studies that demonstrate your AI system performs safely and effectively for its stated purpose and population.

3. 3. Postmarket Monitoring Systems

   5 lessons

   Implement operational systems to detect when AI performance degrades, drifts, or produces safety signals in real-world use.

4. 4. Change Control and Model Updates

   4 lessons

   Manage software and model updates in ways that preserve safety, maintain regulatory compliance, and enable improvement.

5. 5. Evidence Documentation and Submissions

   5 lessons

   Prepare technical files, submissions, and documentation that satisfy regulators and support commercial adoption.

6. 6. Cross-Functional Collaboration and Accountability

   4 lessons

   Coordinate between engineering, clinical, regulatory, and commercial teams to allocate responsibility and manage tradeoffs.

7. 7. Emerging Regulatory Models for Adaptive AI

   5 lessons

   Understand evolving frameworks for continuously learning systems, generative AI, and other adaptive technologies.