Artificial Intelligence Governance and Review at an Academic Healthcare System

As AI and machine learning tools rapidly enter healthcare, their potential to improve care is matched by risks related to bias, safety and accountability. This session presents the development and implementation of an AI governance system within a large academic healthcare system, focused on promoting ethical and responsible AI use. We describe a dual-track process: a Responsible AI Checklist that helps teams self-certify basic compliance and a more detailed AI Risk Analysis Report for high-risk use cases. Drawing on interdisciplinary collaboration, this governance structure balances innovation with oversight while minimizing administrative burden. Participants will learn how a case-law-inspired, iterative approach to governance enables continuous learning and adaptation. Key takeaways include practical tools and best practices for evaluating AI models and fostering shared responsibility among stakeholders. This session offers real-world insights for organizations aiming to ensure that AI-driven innovations are deployed safely, ethically and effectively in healthcare settings.