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Session 8: AI and Machine Learning Innovations Transforming Real-World Data from Patient Insights to Regulatory-Ready Evidence
Session Chair(s)
Rachele Hendricks-Sturrup, DrSc, MA, MSc
Research Director, Real-World Evidence
Duke-Margolis Institute For Health Policy, United States
Artificial intelligence and machine learning are transforming the use of real-world data (RWD) throughout the evidence generation lifecycle. Innovations include AI-powered enhancement of unstructured clinical data to fill patient journey gaps, advanced applications in pharmacovigilance for faster, more accurate safety insights, and machine learning methods to optimize clinical trial feasibility by refining target patient populations. Speakers will share case studies and practical approaches driving regulatory-ready real-world evidence.
Learning Objective : - Demonstrate how AI tools can fill gaps in unstructured clinical data and improve patient journey insights within the session timeframe
- Apply knowledge of ML methods to optimize target patient populations and improve clinical trial feasibility during the session
- Explain key AI and ML applications that enhance real-world data for study design and pharmacovigilance by the end of the session
Speaker(s)
Filling in Gaps in Patient Journeys: Using AI to Enhance RWD
Carl Marci, MD
OM1, United States
Chief Clinical Officer and Managing Director, Mental Health and Neuroscience
The Use of Artificial Intelligence for Real-World Evidence in Pharmacovigilance
Juhaeri Juhaeri, PhD
Sanofi, United States
Vice President and Global Head, Epidemiology and Benefit-Risk Evaluation
Novel Applications of Machine Learning Methods in Real-World Data in Clinical Trial Operations: Clinical Trials Feasibility
Sherrine Eid, MPH
SAS Institute Inc., United States
Global Head, Epidemiology, RWE and Observational Research
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