Explore the regulatory, ethical, and operational implications of AI's inherent error potential — and build a defensible framework for compliant AI integration in FDA-regulated environments.
Includes live access & recording
Artificial Intelligence (AI) is rapidly transforming regulated industries, including pharmaceuticals, medical devices, and biologics. From predictive analytics and batch record review to deviation trending and inspection readiness, AI offers unprecedented efficiency. However, one fundamental reality remains: AI systems are not error-free — and may never be.
Unlike traditional software, AI systems — especially machine learning and generative AI — operate probabilistically. This means outputs can vary, contain bias, hallucinate information, or produce inconsistent results. In highly regulated environments governed by agencies such as the U.S. Food and Drug Administration, even small inaccuracies can have major compliance and patient safety implications.
This session explores the regulatory, ethical, and operational implications of AI's inherent error potential. Participants will gain clarity on validation expectations, risk management strategies, and how to responsibly integrate AI within FDA-regulated systems while maintaining GMP compliance and data integrity. Rather than asking whether AI can be perfect, this course reframes the question: How do we build controls, oversight, and governance models that make AI safe, compliant, and inspection-ready?
AI adoption in regulated industries is accelerating — but regulatory expectations remain stringent. Understanding how AI errors intersect with GMP requirements, validation standards, and FDA scrutiny is essential before implementation.
The FDA has signaled growing interest in AI governance, transparency, and lifecycle oversight. Organizations that deploy AI tools without understanding the applicable regulatory framework are placing their operations — and patient safety — at risk.
This session provides a practical framework for deploying AI responsibly in regulated environments without compromising compliance or patient safety.
By the end of this session, participants will be equipped to manage AI risk, meet FDA expectations, and deploy AI responsibly in regulated operations.
Explain why AI errors may be statistically unavoidable.
Differentiate between deterministic software errors and probabilistic AI outputs.
Interpret FDA expectations for AI-enabled tools in GMP environments.
Apply risk-based validation principles to AI systems.
Design oversight mechanisms and human-in-the-loop safeguards.
Identify documentation requirements for AI governance.
Establish monitoring metrics for AI performance drift.
Prepare defensible responses for regulatory inspections involving AI tools.
Five focused areas that will transform how your organization approaches AI compliance and governance.
Understand how FDA expectations apply to AI-enabled systems operating in GMP-regulated environments.
Learn how to assess AI risk using ICH-aligned frameworks appropriate for regulated industries.
Explore limitations of traditional Computer System Validation (CSV) when applied to adaptive AI systems.
Prepare for regulatory questions about algorithm transparency, explainability, and human oversight.
Implement structured human-in-the-loop controls to mitigate AI risk and maintain compliance.
A comprehensive deep-dive across 10 critical areas of AI error management and compliance in regulated environments.
Professionals working in FDA-regulated and GMP environments across quality, compliance, technology, and operations roles.
Quality Assurance (QA) Professionals
Quality Control (QC) Analysts
Regulatory Affairs Specialists
Computer System Validation (CSV) Professionals
IT and Data Governance Leaders
Manufacturing and Operations Managers
Compliance Officers
Risk Management Professionals
Digital Transformation Leaders
As AI tools increasingly support documentation review, predictive maintenance, deviation investigations, and even regulatory submissions, organizations face a new compliance frontier. Unlike traditional automation systems, AI models evolve, retrain, and may produce non-repeatable outputs. This challenges long-standing regulatory paradigms built on consistency and reproducibility.
The FDA has signaled growing interest in AI governance, transparency, and lifecycle oversight. Organizations must shift from a "validate once" mindset to a continuous monitoring and control strategy. This topic builds awareness of AI's structural limitations and provides a defensible framework for compliant integration into regulated operations.
Dr. Ginette Collazo is an Industrial-Organizational Psychologist with 20 years of experience specializing in Engineering Psychology and Human Reliability, disciplines that study the interaction between human behavior and productivity. She has held positions leading training and human reliability programs in the Pharmaceutical and Medical Device Manufacturing Industry.
Nine years ago, Dr. Collazo established Human Error Solutions (HES), a Florida-based boutique consulting firm, where she has positioned herself as one of the few Human Error Reduction Experts in the world. HES developed a unique methodology for human error investigations, cause determination, CAPA development and effectiveness that has been implemented and proven across different industries globally.
She is the author of the book Human Error: Root Cause Determination Model, published in 2008. She is also a speaker at significant events including Interphex, FDAnews Annual Conference, Global Conference on Process Safety, International Conference on Applied Human Factors and Ergonomics, and the Pharmaceutical Industry Association.
Don't miss this critical training on AI's inherent limitations and how to deploy it responsibly in FDA-regulated environments.