Published research and literature-based content holds valuable insight that can inform clinical trial execution and outcomes. While rich with data, this content is unfortunately very challenging to effectively analyze.
Clinical meta-analysis addresses this challenge by extracting, combining and normalizing clinical trial results from a range of data sources that can be used to draw conclusions about therapeutic effectiveness, clinical trial outcomes and/or to plan new studies.
Deep learning large language models (LLMs) and generative pre-trained transformers (GPTs) present a paradigm shift in how the industry can approach clinical trial analysis. The application of these deep learning technologies to the complex content that makes up the clinical trial data landscape is accelerating the collection of valuable insights needed to inform drug efficacy, safety and ultimately overall trial success.
This webinar will explore:
- The role of data in developing effective deep learning-powered meta-analysis datasets
- How AI-powered meta-analysis data will enable trial teams to make changes in real-time to improve study outcomes.
- Modern applications for applying deep learning in clinical outcomes research
