In this study, we aim to enhance radiology reporting by improving both the conciseness and structured organization of findings (also referred to as templating), specifically by organizing information according to anatomical regions. This structured approach allows physicians to locate relevant information quickly, increasing the report's utility. We utilize Large Language Models (LLMs) such as Mixtral, Mistral, and Llama to generate concise, well-structured reports. Among these, we primarily focus on the Mixtral model due to its superior adherence to specific formatting requirements compared to other models. To maintain data security and privacy, we run these LLMs locally behind our institution's firewall. We leverage the LangChain framework and apply five distinct prompting strategies to enforce a consistent structure in radiology reports, aiming to eliminate extraneous language and achieve a high level of conciseness. We also introduce a novel metric, the Conciseness Percentage (CP) score, to evaluate report brevity. Our dataset comprises 814 radiology reports authored by seven board-certified body radiologists at our cancer center. In evaluating the different prompting methods, we discovered that the most effective approach for generating concise, well-structured reports involves first instructing the LLM to condense the report, followed by a prompt to structure the content according to specific guidelines. We assessed all prompting strategies based on their ability to handle formatting issues, reduce report length, and adhere to formatting instructions. Our findings demonstrate that open-source, locally deployed LLMs can significantly improve radiology report conciseness and structure while conforming to specified formatting standards.

本研究旨在通过改进放射学报告中发现的简洁性和结构化组织，以解决信息查找困难的问题。我们采用本地运行的大型语言模型（如Mixtral）来生成简洁且结构良好的报告，并提出了新的“简洁性百分比”指标来评估报告的简洁性。研究结果表明，采用特定提示策略后，能显著提高报告的简洁性和结构，提升放射学报告的实用性。

通过本地大型语言模型改善放射学报告的简洁性和结构