Acceptance Criteria for AI Validation: Determining What "Reliable" Actually Means in GxP Systems

Artificial Intelligence is rapidly being embedded into regulated GxP operations — from quality systems and manufacturing support to clinical, laboratory, and regulatory processes. But one question continues to challenge industry and regulators alike: What does "reliable" actually mean for AI in a regulated environment? Traditional validation approaches were designed for deterministic systems that consistently produce the same output. AI introduces probabilistic behavior, evolving models, hallucinations, bias, performance drift, and opaque decision-making that require a fundamentally different way of defining acceptance criteria. This session explores how life sciences organizations can establish meaningful, risk-based acceptance criteria for AI-enabled systems while remaining compliant with FDA, EMA, MHRA, GAMP®, and data integrity expectations. Attendees will learn how to move beyond checkbox validation and instead define measurable standards for trust, reliability, oversight, explainability, and human review within AI-driven GxP processes. The discussion emphasizes the role of risk management and the critical human guardrail for AI-enabled decision making. WHY YOU SHOULD ATTEND Attendees will gain practical insight into one of the most urgent and misunderstood challenges in regulated AI adoption: How do you prove an AI system is fit for intended use when outputs may vary? Participants will learn how to define defensible AI acceptance criteria aligned to patient safety, product quality, and data integrity; understand the difference between validating deterministic systems versus AI-enabled systems; apply risk-based thinking to AI oversight and validation activities; identify where human review is essential; reduce regulatory exposure associated with unchecked AI output; recognize warning signs of hallucinations, bias, drift, and unreliable AI behavior; build greater trust in AI-assisted GxP processes without creating excessive documentation burden; and prepare for increasing regulatory scrutiny surrounding AI usage in life sciences. This session is especially valuable for organizations actively evaluating or implementing AI tools in regulated environments. Code: FDB3243