"Connecting State-of-the-Art AI Development to Clinical Health Care: Opportunities, Governance, and the Path Forward"
Wednesday, August 12, 2026
International Convention Center Jeju (ICC-Jeju)
224 Jungmungwangwang-ro, Seogwipo City, Jeju, Korea
(Room 400 on 4th Floor of the International Convention Center Jeju, <see ICC-Jeju Floor Plan>)
(see detailed agenda in Scientific Program)
Featured Speakers
AI continues to drive transformation across industries, and its role in biomedicine and healthcare has become increasingly critical. KDD2026 HealthDay is dedicated to exploring how state-of-the-art AI development can connect to the frontlines of clinical health care — from building responsible policy foundations and accelerating biomedical discovery to translating AI innovations into ethical, governance-ready clinical applications. We have invited global leaders from NIH, NSF, leading academic medical centers, and research institutions to share their vision for how AI-driven knowledge discovery can meaningfully improve patient outcomes and health systems worldwide.
Panel Discussions
Open dialogue among all attendees, including prominent investigators, health practitioners, and policymakers in the field, to address the following questions. (see details in the Scientific Program)
Panel Discussion "AI Platforms in Healthcare: Privacy/HIPAA Compliance"
How can national research ecosystems architect federated, privacy-preserving AI frameworks that substantially reduce the need for centralized data storage and the associated risks to individual confidentiality?
Many public and institutional funding efforts in biomedical AI unintentionally reinforce concentration of capability in already dominant healthcare systems because they prioritize scale, centralized data access, and existing infrastructure. Is there room for smaller healthcare entities to represent strategically important innovation environments for federated AI, privacy-preserving computation, rapid implementation, and clinically diverse modeling? If so, how do we begin redesigning both the narrative and the incentive structures so smaller healthcare organizations are recognized as strategic contributors to innovation rather than institutions being left behind by it?
Federal frameworks like HIPAA were written long before large language models and federated learning existed. Where is current regulation most out of date?
Privacy harms in healthcare AI are not evenly distributed; communities historically subjected to medical exploitation face distinct risks. How should privacy frameworks account for the fact that data practices may disproportionately affect communities, even when no identifiable individual harm is apparent?
If you could change one thing (e.g., policy, technical standard, or institutional norm) to meaningfully advance privacy-compliant AI in healthcare within the next two years, what would it be?
Panel Discussion "Enhanced Clinical Data Governance in the Era of Generative AI"
How should data stewardship and security frameworks be updated to account for AI-generated content (e.g., synthetic data, digital twins, agentic AI outputs) entering healthcare systems and public resources?
As AI outputs are increasingly influencing diagnosis and treatment decisions, who is currently accountable when a generative AI tool contributes to an error, and is that accountability structure the right one?
What governance infrastructure needs to exist at the data layer before institutions can safely deploy generative AI at scale? How far are most health systems from having that infrastructure today?
Health data governance is increasingly a cross-border problem as models are trained and deployed across jurisdictions with variable legal frameworks. What does meaningful international coordination look like, and is it realistic?
After a full day of discussion spanning policy, programs, and clinical application: what is the single data governance problem that, if solved, would have the greatest downstream impact on the responsible use of generative AI in biomedicine?