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Wednesday June 18, 2025 1:45pm - 2:45pm EDT
Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-650-L04-P; CME 1.00; RN 1.00

In the rapidly evolving landscape of medical product development, the utilization of synthetic data and digital twins has gained increasing interest as a transformative approach to accelerate the development and approval of medical products. This session aims to explore the potential and innovative applications of these technologies in the realm of drug development, featuring speakers from health authorities, academia and industry. Dr. Khaled El Emam, Professor at the University of Ottawa, will present his latest research on generating synthetic data to augment clinical trials. Additionally, he will explore practical applications of synthetic data generation using real-world data. Dr. Arman Sabbaghi, Associate Professor at Purdue University, with prior experience at Unlearn, will present AI-Generated Digital Twins to deliver more efficient clinical trials. He will discuss the innovative statistical methodologies and novel trial designs that combine historical data, artificial intelligence (AI), and randomization to deliver smaller, faster, and more powerful RCTs that are built with regulatory guidance in mind. Dr. Ye Li, Mathematical Statistician, CDER, FDA will present statistical challenges leveraging machine learning in clinical trials. Several key factors that may influence the application of ML and AI algorithms in analyzing efficacy data will be discussed, including estimand and type I error rate control, as well as the importance of interpretability and reproducibility of ML/AI models to ensure robust and reliable results. Furthermore, she will address corresponding considerations related to these factors when using Digital Twins.

Learning Objectives

Describe what synthetic data and digital twins are; Discuss ways to apply AI to generate synthetic data and digital twin; Illustrate the potential of synthetic data and digital twins in medical product development.

Chair

Di Zhang, PhD

Speaker

Applications of Synthetic Data in Clinical Trials and Real-World Studies
Khaled El Emam, PhD

Statistical Methods for Unleashing AI-Generated Digital Twins to Deliver More Efficient Randomized Controlled Trials
Arman Sabbaghi, PhD, MA

Leveraging Machine Learning in Clinical Trials: Statistical Challenges
Ye Li, PhD


Speakers
KE

Khaled El Emam

Professor, University of Ottawa, Canada
Dr. Khaled El Emam is the Canada Research Chair (Tier 1) in Medical AI at the University of Ottawa, where he is a Professor in the School of Epidemiology and Public Health. He is also a Senior Scientist at the Children’s Hospital of Eastern Ontario Research Institute, and Scholar-in-Residence... Read More →
avatar for Ye Li

Ye Li

Mathematical Statistician, OTS, CDER, FDA, United States
Dr. Ye Li is a mathematical statistician in the Office of Biostatistics, Center for Drug Evaluation and Research, FDA. In this role, she performs statistical reviews on submissions of neurology products. Dr. Li worked in Office of Quality Surveillance, Center for Drug Evaluation and... Read More →
AS

Arman Sabbaghi

Head of Biostatistics Research, Unlearn, United States
avatar for Arman Sabbaghi

Arman Sabbaghi

Associate Professor of Statistics, Purdue University, United States
Dr. Arman Sabbaghi is a creative and principled statistical scientist with over a decade of experience at the intersection of AI and statistical innovation. He has been recognized for pioneering the combination of AI/ML algorithms with causal, Bayesian, and experimental design methods... Read More →
avatar for Di Zhang

Di Zhang

Associate Director of RWE Statistics, Teva, United States
Di Zhang is the Associate Director of RWE Statistics and Data Science at Teva Pharmaceuticals. Her research interests include causal inference, RWE study designs and methods, the utilization of machine learning in clinical trials and real-world applications and leveraging real-world... Read More →
Wednesday June 18, 2025 1:45pm - 2:45pm EDT
144ABC Walter E. Washington Convention Center 801 Allen Y. Lew Place, NW Washington, DC 20001-3614 USA
  11: Statistics-Data Science, Session |   03: Data-Tech, Session
  • Level Basic
  • Level Basic
  • format csv
  • Credit Type ACPE, CME, RN
  • Tags Session

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