AI Errors May Be Impossible to Eliminate — What That Means for Its Use in the FDA

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? Code: FDB1304