Agentic RAG paper tests what works in clinical information extraction

A new arXiv paper studies configurable clinical information extraction using agentic RAG. The paper’s framing is practical: it asks what works, what breaks, and why when retrieval-augmented agents are applied to clinical text.

Clinical extraction is a demanding test bed because errors can be costly and data is often messy, specialized, and privacy-sensitive. Agentic RAG promises more flexible reasoning over documents, but it also introduces more moving parts that can fail.

The work is another sign that AI evaluation is moving toward domain-specific workflows rather than generic benchmarks alone.