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Operationalizing Large Language Models in Drug Development
Session Chair(s)
Wesley Anderson, PHD
Scientist, Quantitative Medicine
Critical Path Institute, United States
Generative AI and large language models (LLMs) are reshaping drug development and Real-World Evidence (RWE). This session explores real-world applications, debunks common myths, and outlines what’s needed for safe, scalable, and compliant use across the drug development lifecycle.
Learning Objective : Distinguish between the current capabilities and limitations of generative AI and large language models (LLMs) in drug development; Identify practical and regulatory challenges in adopting GenAI across evidence generation workflows; Discuss future opportunities for integrating GenAI into the various areas of the drug development pipeline; Identify/mitigate bias in regulated workflows
Speaker(s)
GenAI for literature-based evidence synthesis: From systematic literature review to digitization of trial publications
Emily Nieves, PHD
Delineate, United States
Co-Founder
Panelist
James Lu, PHD
A* Bioinformatics Institute, Philippines
AI Scientist
Panelist
Representative Invited
FDA, United States
Panelist
Jean Stimola-Sposaro, MHS, LLM
Decentralized Trials and Research Alliance (DTRA), United States
Vice President of Memberships and Partnerships
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