Talk AI for Clinical Trials track
CATALYST Trial Timeline Planning—Enhancing Predictions with Advanced Machine Learning and Large Language Models
Wed, Feb 5, 2025 · 11:45 AM–12:05 PM EST Rosen Shingle Creek
About this session
Accurate prediction of trial enrollment timelines at the portfolio-planning stage is essential to strategic planning and the efficient allocation of resources for pharmaceutical sponsors. Traditional methods rely heavily on structured datasets and manual data curation, often resulting in suboptimal outcomes. This presentation introduces an enhanced approach that leverages advanced machine learning (ML) algorithms and large language models (LLMs) to accurately forecast trial enrollment timelines.
