Improving Radiology Report Conciseness and Structure via Local Large Language Models.

ABSTRACT: Radiology reports are often lengthy and unstructured, posing challenges for referring physicians to quickly identify critical imaging findings while increasing risk of missed information. This retrospective study aimed to enhance radiology reports by making them concise and well-structured, with findings organized by relevant organs. To achieve this, we utilized private large language models (LLMs) deployed locally within our institution's firewall, ensuring data security and minimizing computational costs. Using a dataset of 814 radiology reports from seven board-certified body radiologists at [-blinded for review-], we tested five prompting strategies within the LangChain framework. After evaluating several models, the Mixtral LLM demonstrated superior adherence to formatting requirement

SUBMITTER: Hartsock I 

PROVIDER: S-EPMC12921097 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature