Move beyond checkbox validation — learn how to define measurable, risk-based acceptance criteria for AI-enabled GxP systems aligned with FDA, EMA, MHRA, GAMP®, and data integrity expectations.
Includes live access & recording
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. The discussion emphasizes the role of risk management and the critical human guardrail for AI-enabled decision making.
This session is especially valuable for organizations actively evaluating or implementing AI tools in regulated environments. Attendees will gain practical insight into one of the most urgent and misunderstood challenges in regulated AI adoption.
Participants will learn how to define defensible AI acceptance criteria, understand the difference between validating deterministic versus AI-enabled systems, identify where human review is essential, recognize warning signs of hallucinations, bias, and drift, and prepare for increasing regulatory scrutiny surrounding AI usage in life sciences.
You will leave equipped to build greater trust in AI-assisted GxP processes without creating excessive documentation burden — and with the frameworks to reduce regulatory exposure from unchecked AI output.
By the end of this 90-minute session, participants will have practical frameworks for defining, documenting, and defending AI acceptance criteria in FDA and global GxP-regulated environments.
Define defensible AI acceptance criteria aligned to patient safety, product quality, and data integrity
Understand the difference between validating deterministic systems versus AI-enabled probabilistic systems
Apply risk-based thinking to AI oversight and validation activities across GxP functions
Identify where human review is essential and define expert roles within AI-assisted workflows
Reduce regulatory exposure associated with unchecked AI output across quality and manufacturing functions
Recognize warning signs of hallucinations, bias, model drift, and unreliable AI behavior in regulated operations
Build greater trust in AI-assisted GxP processes without creating excessive documentation burden
Prepare for increasing regulatory scrutiny surrounding AI usage in FDA and globally regulated life sciences operations
Four foundational pillars of AI validation that every GxP organization must address before deploying AI in regulated environments.
Why traditional CSV approaches alone are insufficient for probabilistic AI systems — and how to redesign your validation framework to address confidence scoring, performance drift, explainability, and the challenge of defining "consistent" AI behavior.
Concrete examples of AI acceptance criteria — accuracy thresholds, confidence scoring, false positive/negative tolerances, hallucination detection controls, traceability expectations, and escalation trigger definitions that stand up under regulatory review.
Why human oversight remains a regulatory control — not merely a best practice — and how to define expert roles, avoid automation complacency, and balance efficiency with compliance in AI-driven GxP decision making.
Applying FDA Computer Software Assurance (CSA) principles to AI validation — focusing testing effort on high-risk functionality, generating proportionate evidence, and building continuous monitoring programs that detect performance degradation after deployment.
A structured deep-dive from foundational validation paradigm shifts through real-world AI implementation scenarios in GxP-regulated environments.
This session is designed for leaders and practitioners involved in regulated computerized systems, digital transformation, AI governance, and quality oversight.
Quality Assurance and Quality Systems Professionals
Computer System Validation (CSV) and CSA Teams
IT and Digital Transformation Leaders
Regulatory Affairs Professionals
Manufacturing and Operations Leadership
Laboratory Informatics and LIMS Administrators
Clinical Systems and Data Management Professionals
Data Integrity Specialists
Cybersecurity and AI Governance Teams
Compliance Officers and Auditors
Vendors and Consultants Supporting GxP Systems
Executive Leadership Evaluating AI Adoption
"How do you prove an AI system is fit for intended use when outputs may vary?" — This is the central unanswered question facing every regulated organization deploying AI in GxP operations today.
The adoption of artificial intelligence in FDA and globally regulated life sciences operations has accelerated dramatically. AI tools are now embedded in quality management systems, manufacturing execution platforms, laboratory informatics environments, clinical data systems, and regulatory document workflows. The efficiency and pattern-recognition capabilities these systems offer are compelling — but the regulatory framework for validating them remains unsettled.
Unlike traditional computerized systems, AI does not produce deterministic outputs. Given the same input, a large language model, a neural network, or a machine learning classifier may produce different outputs across different sessions, versions, or contexts. Performance may degrade as data distributions shift over time. Outputs may reflect biases embedded in training data. And some AI systems may produce plausible-sounding but factually incorrect outputs — a phenomenon known as hallucination — with no intrinsic error signal to alert the user.
FDA has begun addressing these challenges through emerging AI guidance, Computer Software Assurance (CSA) principles, and warning letters that reference AI-enabled systems. EMA, MHRA, and other global regulators have issued data integrity and AI governance guidance that creates specific expectations for life sciences organizations. GAMP® 5 Second Edition and emerging ISPE guidance documents address AI risk management within the validation lifecycle.
Yet many organizations are discovering that they lack the internal frameworks to translate these evolving regulatory expectations into practical, inspection-ready AI validation programs — particularly when it comes to defining what "reliable" actually means for a system that, by design, does not always produce the same output.
Carolyn Troiano has more than 45 years of experience in computer system validation in the pharmaceutical, medical device, biotechnology, tobacco, and other FDA-regulated industries. She is currently an independent consultant, advising companies on FDA compliance, Computer System Validation (CSV), and large-scale IT system implementation projects.
Carolyn participated in the FDA/Industry Partnership to develop 21 CFR Part 11, the FDA's Guidance for Electronic Records and Electronic Signatures. During her career she has provided training including CSV, 21 CFR Part 11, Data Integrity, and many other related compliance topics of interest to the life science industries.
Her firsthand involvement in shaping foundational FDA electronic records regulations — combined with decades of practical CSV and CSA implementation experience — gives her a uniquely authoritative perspective on how AI validation challenges intersect with existing regulatory frameworks and where the field is heading.
Join this practical 90-minute session on AI acceptance criteria, risk-based validation, and human oversight governance for GxP-regulated environments.