FDA's Computer Software Assurance guidance already gave teams a more efficient path to validation by encouraging a risk-based approach. AI puts that framework to its hardest test yet. When a system's output can shift with every model update, scripted test cases stop being sufficient, and teams are left asking how to prove AI can be trusted in a regulated process. This webinar breaks down how to apply CSA's risk-based principles to AI, and what that looks like in practice, using a validated requirement conflict detection AI agent as a real-world example.
Learning Objectives
Upon completion of this webinar, attendees will understand:
- What changes when validation must account for model drift, prompt sensitivity, and non-deterministic outputs.
- A risk-based framework for evaluating AI for regulated use including accuracy against intended use, level of human oversight, and reproducibility, among other dimensions.
- Strategies to automate validation and reduce manual documentation effort using data from your existing tools.
Who Should Attend?
Quality and regulatory professionals responsible for validation strategy, evidence, and inspection readiness, teams adopting AI under FDA's CSA who need a clear, defensible validation approach and executives looking to accelerate product innovation with AI.
Audience Learning Level
Intermediate: Content is designed based upon the assumption that individuals have basic knowledge of the topic(s) and/or demonstrated competence related to the topic(s). Higher-level concepts are introduced during lectures; exercises requiring synthesis and/or application of concepts are incorporated into the activity.
Proof of Attendance
A certificate of attendance can be downloaded from the RAPS Learning Portal following the event.
Questions
Contact the RAPS Support Center:
Call +1 301 770 2920, ext. 200 (8:30 am–5:30 pm EST, Monday–Friday) or email [email protected].
Speaker
Bailey Canter
