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 BERTtoBERT encoder and decoder architecture. We also provide a data processing pipeline for future models developed on the the MIMIC CXR dataset. 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.