This research demonstrates how retrieval-augmented generation with large language models can accelerate the discovery of causal relationships between Alzheimer's disease biomarkers.

• Leverages scientific literature to identify connections between biomarkers without expensive clinical trials
• Constructs comprehensive causal networks showing how biomarkers influence each other
• Enables earlier detection, precise disease staging, and targeted treatment planning
• Demonstrates practical clinical application of LLMs in medical research

This approach represents a significant advancement for medical professionals by providing a data-driven framework for understanding disease progression pathways and developing more effective diagnostic and treatment protocols.

Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation