Moving Beyond Documentation: How Data Standards Fuel AI Research

Standards have long been viewed as a source of operational friction, yet they are increasingly critical for ensuring that AI systems generate grounded, consistent results. Without established protocols, AI models risk relying on inconsistent terminology, which undermines the integrity of clinical data. By contrast, standardized frameworks allow for the seamless reuse of health information, turning fragmented data into a cohesive engine for discovery.

Key initiatives are already bridging the gap between human readability and machine processing. The ICH M11 framework provides a globally harmonized approach for protocol content, while the EU xSHARE project demonstrates the potential of aligning data across organizations like HL7, CDISC, and ISO. These efforts move beyond simple documentation, providing a robust architecture that supports more efficient statistical programming and research readiness.

The upcoming webinar features industry leaders, including Rebecca Daniels Kush of Catalysis, Ron Fitzmartin of Decision Analytics, Aaron Mann of the Clinical Research Data Sharing Alliance, Dipak Kalra of i~HD, and Sean Connolly of Evinova. The panel will examine the practical hurdles of interoperability and how current initiatives aim to overcome remaining barriers to data sharing. The event is scheduled for 11am EDT on September 3, 2026, offering a look at how foundational architecture will shape the next generation of digital health.