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.

利用一种新的提示策略，结合少样本上下文学习，该论文引入了一种改进放射学报告摘要的方法，通过首先生成普通人摘要来规范关键观察，并使用医患交流启发的非专家通信技术简化复杂信息。我们在MIMIC-CXR、CheXpert和MIMIC-III数据集上评估了这种方法，并将其与7B/8B参数状态下的开源大型语言模型（LLMs），如Meta-Llama-3-8B-Instruct，进行了比较。我们的结果表明，在摘要精确性和可访问性方面都取得了改进，特别是在领域外测试中，某些指标的改进高达5%。

通过与非专业人员总结提示大型语言模型改进专家放射学报告摘要