  Radiology report summarization (RRS) is crucial for patient care, requiring
concise "Impressions" from detailed "Findings." This paper introduces a novel
prompting strategy to enhance RRS by first generating a layperson summary. This
approach normalizes key observations and simplifies complex information using
non-expert communication techniques inspired by doctor-patient interactions.
Combined with few-shot in-context learning, this method improves the model's
ability to link general terms to specific findings. We evaluate this approach
on the MIMIC-CXR, CheXpert, and MIMIC-III datasets, benchmarking it against
7B/8B parameter state-of-the-art open-source large language models (LLMs) like
Meta-Llama-3-8B-Instruct. Our results demonstrate improvements in summarization
accuracy and accessibility, particularly in out-of-domain tests, with
improvements as high as 5% for some metrics.
