Agentic RAG system achieves 96.5% clinician acceptance in clinical data extraction

Researchers have developed an Agentic Clinical Information Extraction (ACIE) system to address limitations in standard retrieval-augmented generation (RAG) for complex patient data. This on-premise RAG pipeline, deployed at University Medicine Essen, is designed to process extensive patient contexts, including temporal reasoning and cross-document dependencies. In a validation study for a lymphoma registry, clinicians verified ACIE's extractions, accepting 96.5% of the data, with acceptance rates varying by extraction type. AI

IMPACT This agentic RAG approach could improve the accuracy and reliability of AI systems in processing complex, heterogeneous clinical data.

RANK_REASON The cluster contains a research paper detailing a new system and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

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• ACIE
• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• Osman Alperen Koraş
• ScienceCast
• University Medicine Essen

• paper
• product
• infra

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