  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 introduced, decomposing the task into error detection,
localization, and correction subtasks, which enhances the explainability and
performance of the system. The effectiveness of the approach is evaluated using
a benchmark dataset created by corrupting real-world radiology reports with
realistic errors, guided by domain experts. Experimental results demonstrate
the benefits of the proposed methods, with the combination of internal and
external retrieval significantly improving the accuracy of error detection,
localization, and correction across various state-of-the-art LLMs. The findings
contribute to the development of more robust and reliable error correction
systems for clinical documentation.
