The impression section of a radiology report summarizes important radiology
findings and plays a critical role in communicating these findings to
physicians. However, the preparation of these summaries is time-consuming and
error-prone for radiologists. Recently, numerous models for radiology report
summarization have been developed. Nevertheless, there is currently no model
that can summarize these reports in multiple languages. Such a model could
greatly improve future research and the development of Deep Learning models
that incorporate data from patients with different ethnic backgrounds. In this
study, the generation of radiology impressions in different languages was
automated by fine-tuning a model, publicly available, based on a multilingual
text-to-text Transformer to summarize findings available in English,
Portuguese, and German radiology reports. In a blind test, two board-certified
radiologists indicated that for at least 70% of the system-generated summaries,
the quality matched or exceeded the corresponding human-written summaries,
suggesting substantial clinical reliability. Furthermore, this study showed
that the multilingual model outperformed other models that specialized in
summarizing radiology reports in only one language, as well as models that were
not specifically designed for summarizing radiology reports, such as ChatGPT.

通过 fine-tuning 多语言 Transformer 模型，该研究实现了自动化生成多种语言的放射学报告总结，结果显示出与人工撰写总结相当甚至更好的临床可靠性。

多语种放射学报告的自然语言处理模型 -- 只需摘要！

Multilingual Natural Language ProcessingModel for Radiology Reports --  The Summary is all you need!

The IMPRESSIONS section of a radiology report about an imaging study is a
summary of the radiologist's reasoning and conclusions, and it also aids the
referring physician in confirming or excluding certain diagnoses. A cascade of
tasks are required to automatically generate an abstractive summary of the
typical information-rich radiology report. These tasks include acquisition of
salient content from the report and generation of a concise, easily consumable
IMPRESSIONS section. Prior research on radiology report summarization has
focused on single-step end-to-end models -- which subsume the task of salient
content acquisition. To fully explore the cascade structure and explainability
of radiology report summarization, we introduce two innovations. First, we
design a two-step approach: extractive summarization followed by abstractive
summarization. Second, we additionally break down the extractive part into two
independent tasks: extraction of salient (1) sentences and (2) keywords.
Experiments on English radiology reports from two clinical sites show our novel
approach leads to a more precise summary compared to single-step and to
two-step-with-single-extractive-process baselines with an overall improvement
in F1 score Of 3-4%.

本研究提出一种新的基于层级结构的放射学报告摘要方法，将提取性摘要分解为关键句和关键词的提取任务，并采用两步法的方式进行生成，实验结果显示此方法与传统单一或双一的实验方法相比，能够更准确地生成摘要，总体 F1 得分提高了 3-4%。