Current deep learning models trained to generate radiology reports from chest radiographs are capable of producing clinically accurate, clear, and actionable text that can advance patient care. However, such systems all succumb to the same problem: making hallucinated references to non-existent prior reports. Such hallucinations occur because these models are trained on datasets of real-world patient reports that inherently refer to priors. To this end, we propose two methods to remove references to priors in radiology reports: (1) a GPT-3-based few-shot approach to rewrite medical reports without references to priors; and (2) a BioBERT-based token classification approach to directly remove words referring to priors. We use the aforementioned approaches to modify MIMIC-CXR, a publicly available dataset of chest X-rays and their associated free-text radiology reports; we then retrain CXR-RePaiR, a radiology report generation system, on the adapted MIMIC-CXR dataset. We find that our re-trained model--which we call CXR-ReDonE--outperforms previous report generation methods on clinical metrics, achieving an average BERTScore of 0.2351 (2.57% absolute improvement). We expect our approach to be broadly valuable in enabling current radiology report generation systems to be more directly integrated into clinical pipelines.

本研究提出了两种方法来消除放射学报告中引用先前报告的问题，一种是基于GPT-3的few-shot方法来重写没有引用先前报告的医疗报告，另一种是基于BioBERT的标记分类方法来直接删除引用先前报告的单词。研究者们认为在这些方法的基础上，CXR-ReDonE模型能够更好地生成放射学报告，进而在临床管道中得到更好的应用。

通过删除虚构的对不存在的先验的引用来改进放射学报告生成系统