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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification
%A Yang, Ziyu
%A Cherian, Santhosh
%A Vucetic, Slobodan
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F yang-etal-2024-two
%X 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.
%U https://aclanthology.org/2024.findings-acl.279
%P 4701-4714

Markdown (Informal)

[Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification](https://aclanthology.org/2024.findings-acl.279) (Yang et al., Findings 2024)

• Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification (Yang et al., Findings 2024)

ACL

• Ziyu Yang, Santhosh Cherian, and Slobodan Vucetic. 2024. Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification. In Findings of the Association for Computational Linguistics ACL 2024, pages 4701–4714, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.