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The impression section of a radiology report summarizes important radiologyfindings and plays a critical role in communicating these findings tophysicians. However, the preparation of these summaries is time-consuming anderror-prone for radiologists. Recently, numerous models for radiology reportsummarization have been developed. Nevertheless, there is currently no modelthat can summarize these reports in multiple languages. Such a model couldgreatly improve future research and the development of Deep Learning modelsthat incorporate data from patients with different ethnic backgrounds. In thisstudy, the generation of radiology impressions in different languages wasautomated by fine-tuning a model, publicly available, based on a multilingualtext-to-text Transformer to summarize findings available in English,Portuguese, and German radiology reports. In a blind test, two board-certifiedradiologists 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 showedthat the multilingual model outperformed other models that specialized insummarizing radiology reports in only one language, as well as models that werenot specifically designed for summarizing radiology reports, such as ChatGPT.

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