This session will demonstrate how AI-powered tools can solve critical regulatory workflow challenges by organizing and making searchable the vast amounts of siloed regulatory data that typically require experienced professionals to navigate. Attendees will learn practical applications through real-world case studies showing how regulatory teams have used AI tools to accelerate predicate device identification, respond effectively to FDA queries with data-backed precedents, conduct comprehensive literature reviews and adverse event analyses, and achieve significant time and cost savings (including one example of $300,000 in avoided testing costs). The presentation emphasizes moving beyond AI hype to focus on concrete, actionable tools that augment daily regulatory work, enabling faster decision-making and reducing dependence on institutional knowledge alone. Participants will gain insights into how these technologies can streamline their regulatory processes and improve submission outcomes.
Learning Objectives:
Identify key regulatory workflow inefficiencies that can be addressed through AI-powered tools, including predicate searches, literature reviews, and adverse event analysis.
Apply AI tools strategically to respond to FDA queries and requests by leveraging precedent analysis and comparative data to support regulatory arguments and potentially avoid costly additional testing requirements.
Evaluate the practical benefits of implementing AI-powered regulatory databases in their organizations, including quantifiable time savings (from days to hours for complex searches) and cost avoidance opportunities demonstrated through real-world case studies.
