Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why
arXiv:2606.19602v1 Announce Type: new Abstract: Patient contexts span hundreds of heterogeneous documents and thousands of structured data points, yet the document-level metadata that AI systems need for retrieval and triage is absent or incomplete. Standard retrieval-augmented generation fails on this data, mishandling temporal reasoning, cross-document dependencies, and missing metadata. We deploy ACIE (Agentic Clinical Information Extraction) at University Medicine Essen: an on-premise agentic RAG pipeline that reasons over complete patient contexts and grounds every answer in source passag
The proliferation of advanced retrieval-augmented generation (RAG) models is exposing limitations in handling complex, heterogeneous real-world data like patient contexts, leading to innovative solutions such as agentic RAG.
This development indicates a crucial step towards robust and reliable AI systems in critical domains like healthcare, where data completeness and accurate reasoning are paramount.
Traditional RAG approaches are proving insufficient for complex clinical data, necessitating agentic RAG systems for accurate information extraction and temporal-aware reasoning.
- · Healthcare AI developers
- · Hospitals and medical institutions
- · Patients benefiting from improved AI diagnostics
- · Developers relying solely on basic RAG models
- · Systems unable to integrate heterogeneous data
Agentic RAG will improve automated clinical information extraction and reasoning, leading to better diagnostic support and treatment planning.
The success of agentic RAG in healthcare will accelerate its adoption in other complex data environments, enhancing decision-making across industries.
Increased reliability of AI in sensitive fields could lead to greater public trust and broader integration of AI agents into daily professional workflows.
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Read at arXiv cs.AI