LLM pipeline extracts clinical data from nurse-patient transcripts

Researchers have developed a retrieval-augmented generation (RAG) pipeline to extract structured clinical information from nurse-patient conversations. This system, utilizing models like Llama-4-Scout and GPT-5.2, aims to reduce clinician documentation burden by normalizing narratives into a predefined schema. The best configuration achieved an 80.36% F1 score, demonstrating that RAG consistently improves performance and that schema constraints can be optimized based on the specific model used. AI

影响 This approach could significantly reduce clinician documentation time, freeing up more time for direct patient care.

排序理由 The cluster contains an academic paper detailing a new method for information extraction using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

• arXiv
• GPT-5.2
• Llama-4-Scout-17B-16E-Instruct
• MEDIQA-SYNUR

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