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ACIE, an agentic RAG system for clinical information extraction, achieves 96.5% acceptance rate in nuclear-medicine physicians' judgments across 7,326 instances, addressing challenges of heterogeneous patient contexts and missing metadata.
This paper presents a modular retrieval-augmented generation (RAG) pipeline for extracting structured clinical observations from conversational nurse-patient transcripts, using schema-constrained prompting and second-pass auditing with Llama and GPT backbones, achieving 80.36% F1 score.