Harnessing Knowledge Retrieval with Large Language Models for Clinical
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Harnessing Knowledge Retrieval with Large Language Models for Clinical
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This study proposes an approach for error correction in clinical radiology reports, leveraging large language models (LLMs) and retrieval-augmented generation (RAG) techniques. The proposed framework employs internal and external retrieval mechanisms to extract relevant medical entities and relations from the report and external knowledge sources. A three-stage inference process is …