Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why
ACIE, an agentic RAG system deployed in a clinical setting, demonstrates high accuracy in extracting medical information from complex patient contexts, achieving 96.
Hugging Face · Daily Papers
·Osman Alperen Çinar-Koraş, Marie Bauer
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·▲ 5 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Osman Alperen Çinar-Koraş, Marie Bauer, Sameh Khattab, Merlin Engelke, Moon Kim, Stephan Settelmeier
- 5 upvotes da comunidade
- Temas: retrieval-augmented generation, agentic RAG pipeline, clinical information extraction, patient contexts, source passages, clinician verification
Resumo
Resumo original (em inglês), extraído do paper:
ACIE, an agentic RAG system deployed in a clinical setting, demonstrates high accuracy in extracting medical information from complex patient contexts, achieving 96.5% acceptance rate by nuclear-medicine physicians across 7,326 judgments.
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