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 
• 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.

_{AREAS COVERED:}

1. Why AI Changes the Validation Paradigm
   • Deterministic systems vs. probabilistic AI systems 
   • Why traditional CSV approaches alone are insufficient 
   • The challenge of defining “consistent” AI behavior 
   • FDA and global regulatory expectations emerging around AI oversight                                                                                                                                                                               
2. Defining “Reliable” in a GxP Environment
   • Reliability vs. accuracy vs. consistency 
   • Risk-based interpretation of acceptable AI performance 
   • Context matters: low-risk vs. high-risk GxP decisions 
   • Establishing intended use boundaries for AI systems                                                                                                                                                                                               
3. Acceptance Criteria for AI Systems
   • Examples of measurable acceptance criteria:
   • Accuracy thresholds 
   • Confidence scoring 
   • False positive/false negative tolerances 
   • Hallucination detection controls 
   • Explainability requirements 
   • Traceability and auditability expectations 
   • Human review requirements 
   • Escalation triggers and exception handling                                                                                                                                                                                                          
4. Managing AI Risks in Regulated Operations
   • Hallucinations 
   • Bias and hidden assumptions 
   • Model drift and performance degradation 
   • Incomplete or poor-quality training data 
   • Data integrity concerns 
   • Overreliance on AI-generated output 
   • Cybersecurity and AI manipulation risks                                                                                                                                                                                                         
5. The Human Guardrail
   • Why human oversight remains essential 
   • Critical thinking as a regulatory control 
   • Defining expert's roles and responsibilities 
   • Avoiding “automation complacency” 
   • Balancing efficiency with compliance and trust                                                                                                                                                                                              
6. CSA and Risk-Based AI Assurance
   • Applying FDA CSA principles to AI validation 
   • Leveraging critical thinking over excessive documentation 
   • Focusing testing effort on high-risk functionality 
   • Evidence generation strategies for AI systems                                                                                                                                                                                               
7. Practical Implementation Strategies
   • Building AI governance frameworks 
   • Defining AI validation lifecycle activities 
   • Monitoring AI after deployment 
   • Periodic review and continuous assurance 
   • Documentation expectations for inspections and audits                                                                                                                                                                               
8. Real-World Scenarios and Discussion
   • AI-generated SOPs and specifications 
   • AI-assisted deviation/CAPA investigations 
   • AI in quality review and data analysis 
   • AI-driven validation documentation generation 
   • Lessons learned from emerging regulatory observations and industry experiences

_{WHO SHOULD ATTEND:}

This session is designed for leaders and practitioners involved in regulated computerized systems, digital transformation, AI governance, and quality oversight, including:

• Quality Assurance and Quality Systems professionals 
• Computer System Validation (CSV) and Computer Software Assurance (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 in regulated operations

Course Director: CAROLYN TROIANO

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.