Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification
<|reference_start|>Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification: 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.<|reference_end|>
@article{yang2024two-pronged,
    title={Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology
  Report Simplification},
    author={Ziyu Yang, Santhosh Cherian, Slobodan Vucetic},
    journal={arXiv preprint arXiv:2406.18859},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18859},
    primaryClass={cs.CL cs.AI}
}