Join national leaders in health policy, regulation, standards development and clinical innovation for the workshop "From Policy to Practice: Governing AI Across the Health Ecosystem — Standards, Systems, and the Patient at the Center" at Mayo Clinic's AI Research Summit, June 4–5 in Rochester, Minnesota, and online. The workshop will examine not what policy says, but what it takes to make it work in practice. Panelists will explore how federal AI strategy translates into real-world health system decisions and how clinicians integrating AI into care workflows and patients who are the ultimate beneficiaries are at the center. Ikram Khan, managing director, Health AI Institute, will moderate a discussion with: • Jesse Isaacman-Beck, Ph.D., director, AI Policy and Strategy, U.S. Department of Health and Human Services Office of the National Coordinator for Health Information Technology • Ram D. Sriram, Ph.D., senior scientific adviser, National Institute of Standards and Technology • Sonja Fulmer, Ph.D., senior director, Health Policy Strategy, Mayo Clinic • Sophie Bakri, M.D., medical director, Center for Digital Health, and Whitney and Betty MacMillan Professor of Ophthalmology in Honor of Robert R. Waller, M.D., Mayo Clinic This session is among 14 workshops offered at the Research AI Summit, along with keynote presentations, lightning talks, poster sessions and other opportunities to connect and collaborate. Geared for researchers, clinicians, innovators and students, this year's event will focus on how multi-agent modeling and simulation can help generate clinically meaningful, real-world evidence. Register by June 1: https://ai-summit.com/ #AIResearchSummit #MayoClinic #AI
One question I'd ask this panel: Who represents implementation? Healthcare AI discussions often include regulators, policymakers, standards bodies, researchers, and technology leaders. Yet many AI initiatives don't fail because of policy. They fail because they don't fit the realities of care delivery. Workflow friction. Alert fatigue. Ownership ambiguity. Training gaps. Poor integration. Lack of trust. We're getting very good at discussing AI governance. I'm not sure we're spending enough time discussing AI adoption. Because in healthcare, an AI tool that isn't used safely and consistently may be perfectly governed and still create no value.