  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.
