Radiology reports are highly technical documents aimed primarily at doctor-doctor communication. There has been an increasing interest in sharing those reports with patients, necessitating providing them patient-friendly simplifications of the original reports. This study explores the suitability of large language models in automatically generating those simplifications. We examine the usefulness of chain-of-thought and self-correction prompting mechanisms in this domain. We also propose a new evaluation protocol that employs radiologists and laypeople, where radiologists verify the factual correctness of simplifications, and laypeople assess simplicity and comprehension. Our experimental results demonstrate the effectiveness of self-correction prompting in producing high-quality simplifications. Our findings illuminate the preferences of radiologists and laypeople regarding text simplification, informing future research on this topic.

翻译：放射学报告是高度专业化的文档，主要服务于医生间的沟通。近年来，将此类报告分享给患者的需求日益增长，这要求为患者提供对原始报告的友好型简化版本。本研究探讨了大型语言模型在自动生成此类简化报告方面的适用性。我们检验了思维链提示与自校正提示机制在该领域的应用价值。同时，我们提出了一种融合放射科医师与普通受众的新型评估方案：放射科医师负责验证简化内容的医学事实准确性，普通受众则评估文本的简明性与可理解性。实验结果表明，自校正提示机制能有效生成高质量的简化报告。本研究揭示了放射科医师与普通受众对文本简化的偏好差异，为该领域的后续研究提供了重要参考。