AI & regulatory quality: proposed training and discussion

AI is a useful topic for practical, evidence-led quality discussions. Our proposed sessions focus on what a tool is intended to do, which evidence supports its output, who reviews the result, and how a change is controlled. These are independent training proposals, not FDA-hosted events or a claim that AI use is automatically acceptable.

Proposed session paths

• AI in the quality unit: source verification, summaries, and accountable human review.
• AI-enabled devices: lifecycle, data, performance, and change questions.
• AI in contract laboratories: original data, methods, reporting, and client responsibilities.
• Software assurance: intended use and risk in device production and quality systems.

Start with official source context

FDA maintains resources for AI-enabled medical devices and AI in drug development. Check the current status and scope of each guidance before relying on it. A discussion paper, draft guidance, final guidance, and binding regulation are different kinds of authority.

Browse proposed AI sessions · Register AI quality interest · Read software and records briefs