Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!
This addresses the time-consuming and error-prone task for radiologists by enabling multilingual summarization, which could improve research and model development for diverse patient data, though it is incremental as it builds on existing multilingual Transformers.
The study tackled the lack of multilingual models for summarizing radiology reports by fine-tuning a multilingual Transformer to generate impressions in English, Portuguese, and German, achieving at least 70% quality matching or exceeding human-written summaries in blind tests and outperforming specialized single-language models and ChatGPT.
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.