Closing the Loop: Automated Semantic Traceability from Study Design to Analysis - Insights from the CDISC AI Innovation Challenge

Automated semantic traceability uses AI and machine learning to link statistical analyses back to their original data sources and definitions within a digital protocol, enabling transparent, machine‑readable data lineage.

This webinar highlights approaches presented in Use Case #3 of the 2025 CDISC AI Innovation Challenge, where participants demonstrated how CDISC Standards -  including USDM, CDASH, SDTM, ADaM, Biomedical Concepts, and dataset specializations - can support end‑to‑end traceability, lineage visualization, and automated downstream outputs in clinical data workflows.

Agenda

• Overview of Automated Semantic Traceability Acceleration Challenge and Key Learnings
• Automated Semantic Traceability Demonstration #1: Merck
• Automated Semantic Traceability Demonstration #2: Zifo
• Q&A Session