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.

本研究探讨大型语言模型在自动生成适合患者的放射学报告简化方面的适用性，研究结果表明自我纠正提示能够产生高质量的简化报告，为放射科医生和一般人对于文本简化的喜好提供了启示，对未来的研究有指导意义。