Improving Radiology Report Conciseness and Structure via Local Large Language Models

TL;DR

This study demonstrates that local large language models can effectively condense and organize radiology reports, reducing verbosity by over 53% and improving clarity without compromising critical information.

Contribution

The paper introduces a novel approach using private, locally deployed LLMs with specific prompting strategies to enhance radiology report conciseness and structure.

Findings

Mixtral LLM outperformed other models in formatting adherence.

Optimal prompting strategy involved report condensation followed by structured formatting.

Reduces redundant words by more than 53% across reports.

Abstract

Radiology reports are often lengthy and unstructured, posing challenges for referring physicians to quickly identify critical imaging findings while increasing the 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 Moffitt Cancer Center, we tested five prompting strategies within the LangChain framework. After evaluating several models, the Mixtral LLM demonstrated superior adherence to formatting requirements compared to alternatives like Llama. The optimal strategy involved condensing reports first and then applying structured formatting based on specific instructions, reducing verbosity while improving clarity. Across all radiologists and reports, the Mixtral LLM reduced redundant word counts by more than 53%. These findings highlight the potential of locally deployed, open-source LLMs to streamline radiology reporting. By generating concise, well-structured reports, these models enhance information retrieval and better meet the needs of referring physicians, ultimately improving clinical workflows.