  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.
