Leveraging summary of radiology reports with transformers

Two fundamental problems in health-care stem from patient handoff and triage. Doctors are often required to perform complex findings summarization to facilitate efficient communication with specialists and decision-making on the urgency of each case. To address these challenges, we present a state-of-the-art radiology report summarization model utilizing adjusted bidirectional encoder representation from transformers BERT-to-BERT encoder–decoder architecture. Our approach includes a novel method for augmenting medical data and a comprehensive performance analysis. Our best-performing model achieved a recall-oriented understudy for gisting evaluation-L F1 score of 58.75/100, outperforming specialized checkpoints with more sophisticated attention mechanisms. We also provide a data processing pipeline for future models developed on the MIMIC-chest X-ray dataset. The model introduced in this paper demonstrates significantly improved capacity in radiology report summarization, highlighting the potential for ensuring better clinical workflows and enhanced patient care.