Ask the Expert webinar series

How is AI Transforming Clinical Data Programming?

Watch this webinar to explore how clinical trials can be accelerated while improving consistency, quality and regulatory compliance.

On-Demand: (Originally presented: August 4, 2026) •  Cost: Complimentary

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About the webinar

Today’s clinical trials face bottlenecks and delays, particularly manual and time-intensive steps like SDTM mapping and code generation. We’ll show you how SAS Clinical Data Transformation modernizes this process – saving valuable time while improving consistency, quality and compliance.

You will learn how:

  • Clinical data transformation is a major, manual bottleneck driving costs, delays and risks.
  • The SAS CDT AI-assisted, human-in-the-loop approach automates CDISC mapping and code generation while preserving transparency and control. 
  • Modernizing this step helps organizations accelerate time to submission while improving consistency, quality and regulatory compliance.

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About the experts


Kim Peplinski

Senior Product Manager, Life Science Solutions, SAS

Kim specializes in clinical trial technologies and product innovation. She has over a decade of experience from roles at YPrime, Merge Healthcare (an IBM company) and PRA Health Sciences, with a focus on IRT systems, product strategy and system design. Kim holds dual bachelor’s degrees in psychology and human biology, along with a minor in genetics from North Carolina State University. Kim is known for bridging technical and commercial domains, driving innovative clinical trial technology, and delivering on complex product roadmaps through cross-functional collaboration.


Pankaj Attri

Solutions Architect, SAS

Pankaj is an AI/ML solutions architect focused on leveraging advanced data science and artificial intelligence to support customers in the life sciences and health care industries at SAS. He partners with these organizations to design and implement state-of-the-art custom solutions that accelerate innovation across research, development and clinical operations. This includes supporting critical workflows such as clinical trial data submission processes, as well as broader applications of AI and data management across drug discovery, diagnostics and health care delivery.

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