Automatic structuring of radiology reports with on-premise open-source large language models.

ABSTRACT:

Objectives

Structured reporting enhances comparability, readability, and content detail. Large language models (LLMs) could convert free text into structured data without disrupting radiologists' reporting workflow. This study evaluated an on-premise, privacy-preserving LLM for automatically structuring free-text radiology reports.

Materials and methods

We developed an approach to controlling the LLM output, ensuring the validity and completeness of structured reports produced by a locally hosted Llama-2-70B-chat model. A dataset with de-identified narrative chest radiograph (CXR) reports was compiled retrospectively. It included 202 English reports from a publicly available MIMIC-CXR dataset and 197 German reports from our university hospital. Senior radiologist prepared a

SUBMITTER: Woznicki P 

PROVIDER: S-EPMC11913902 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature