Enhancing Radiology Clinical Histories Through Transformer-Based Automated Clinical Note Summarization.

Insufficient clinical information provided in radiology requests, coupled with the cumbersome nature of electronic health records (EHRs), poses significant challenges for radiologists in extracting pertinent clinical data and compiling detailed radiology reports. Considering the challenges and time involved in navigating electronic medical records (EMR), an automated method to accurately compress the text while maintaining key semantic information could significantly enhance the efficiency of radiologists' workflow. The purpose of this study is to develop and demonstrate an automated tool for clinical note summarization with the goal of extracting the most pertinent clinical information for the radiological assessments. We adopted a transfer learning methodology from the natural language processing domain to fine-tune a transformer model for abstracting clinical reports. We employed a dataset consisting of 1000 clinical notes from 970 patients who underwent knee MRI, all manually summarized by radiologists. The fine-tuning process involved a two-stage approach starting with self-supervised denoising and then focusing on the summarization task. The model successfully condensed clinical notes by 97% while aligning closely with radiologist-written summaries evidenced by a 0.9 cosine similarity and a ROUGE-1 score of 40.18. In addition, statistical analysis, indicated by a Fleiss kappa score of 0.32, demonstrated fair agreement among specialists on the model's effectiveness in producing more relevant clinical histories compared to those included in the exam requests. The proposed model effectively summarized clinical notes for knee MRI studies, thereby demonstrating potential for improving radiology reporting efficiency and accuracy.