LIVE WEBINAR
Acceptance Criteria for AI Validation
Determining What “Reliable” Actually Means in GxP Systems
- July 22, 2026
- 12 PM Eastern Time US
- 4 PM GMT
- 90 Minutes
ABOUT THE COURSE
Organizations adopting AI must establish measurable acceptance standards before AI-supported outputs become part of regulated GxP decisions and quality activities. This session organizes practical approaches for defining acceptance criteria, validation evidence, human oversight, and ongoing monitoring across AI-enabled GxP applications.
The session begins by distinguishing traditional validation from AI-specific validation expectations, then develops practical methods for defining reliability, intended use, measurable acceptance criteria, and human oversight. It concludes with governance, monitoring, and implementation practices that support continued assurance as AI-enabled systems evolve throughout their operational lifecycle.
Designed for organizations evaluating, validating, governing, or overseeing AI-enabled systems supporting regulated quality, manufacturing, laboratory, and compliance activities.
KEY AREAS COVERED
- Acceptance criteria for probabilistic AI systems
- Risk-based reliability and intended use boundaries
- Human review, escalation, and oversight expectations
- Managing hallucinations, bias, drift, and data integrity risks
- Applying CSA principles to AI validation activities
- Documentation, monitoring, and inspection readiness practices
Carolyn Troiano
Carolyn Troiano brings more than 45 years of experience in computer system validation across pharmaceutical, medical device, biotechnology, and other FDA-regulated industries. As a contributor to the FDA/Industry Partnership that developed 21 CFR Part 11 and a long-standing advisor on CSV, data integrity, and AI-enabled systems, she provides practical guidance on validating emerging technologies within regulated environments.
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