AI-Powered ADaM Autogeneration: Standardize Logic, Streamline Analysis, and Accelerate Submission
Creating ADaM datasets is a critical and expert-driven step in clinical trial analysis, but it’s often time-consuming, error-prone, and resource-intensive. Translating SDTM data into ADaM formats requires in-depth therapeutic area knowledge, careful derivation planning, and strict adherence to CDISC and sponsor-specific standards. Each study demands a customized approach, and even small changes can ripple across domains.
But what if Generative AI could take on that complexity and generate high-quality, audit-ready ADaM programs in a fraction of the time?
In this webinar, you’ll learn:
- How AI enables intelligent planning across the 7-step ADaM lifecycle
- How reusable code modules adapt to study-specific needs without starting from scratch
- How GenAI bridges gaps between statistical planning, programming, and QC
