Learning to Generate Radiology Findings from Impressions Based on Large Language Model

Medical imaging plays a pivotal role in clinical diagnosis, and the textual reports associated with these images are of paramount importance in aiding image comprehension and supporting treatment decisions. Automated report generation serves to alleviate the burden on radiologists and has garnered significant attention in the field of medical artificial intelligence. Previous research in text-based report generation primarily focused on generating impressions statements from radiology findings. However, the benefits in terms of reducing the workload on radiologists were not particularly evident. In this article, we propose a novel task of generating findings from radiology impressions. Leveraging advanced large language models, we trained a set of report generation models using a real dataset of knee MRI reports. Additionally, we incorporated various strategies, including data augmentation and efficient parameter fine-tuning. Objective experiments affirm the effectiveness of the methods we introduced. Furthermore, we conducted subjective assessments by radiologists, and the results demonstrate that our trained large language models significantly outperform professional radiologists in terms of overall report quality and content consistency.