Automated Structured Radiology Report Generation with Rich Clinical
  Context
Authors: name: Seongjae Kang, name: Dong Bok Lee, name: Juho Jung, name: Dongseop Kim, name: Won Hwa Kim, name: Sunghoon Joo

Automated structured radiology report generation (SRRG) from chest X-ray
images offers significant potential to reduce workload of radiologists by
generating reports in structured formats that ensure clarity, consistency, and
adherence to clinical reporting standards. While radiologists effectively
utilize available clinical contexts in their diagnostic reasoning, existing
SRRG systems overlook these essential elements. This fundamental gap leads to
critical problems including temporal hallucinations when referencing
non-existent clinical contexts. To address these limitations, we propose
contextualized SRRG (C-SRRG) that comprehensively incorporates rich clinical
context for SRRG. We curate C-SRRG dataset by integrating comprehensive
clinical context encompassing 1) multi-view X-ray images, 2) clinical
indication, 3) imaging techniques, and 4) prior studies with corresponding
comparisons based on patient histories. Through extensive benchmarking with
state-of-the-art multimodal large language models, we demonstrate that
incorporating clinical context with the proposed C-SRRG significantly improves
report generation quality. We publicly release dataset, code, and checkpoints
to facilitate future research for clinically-aligned automated RRG at
https://github.com/vuno/contextualized-srrg.