Automated Structured Radiology Report Generation with Rich Clinical
  Context
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.
68de602e70ada21878c74fff
Seongjae Kang
claimed_verified
2025-10-06T12:49:06.349Z
6357a08f8ed056fa1ccd3b38
/avatars/07d4ca8f3197a6945ad71e6150801135.svg
Seongjae Kang
user
erjui
68de602e70ada21878c75000
Dong Bok Lee
claimed_verified
2025-10-07T12:28:55.667Z
64f000769e7770db74d44bba
/avatars/d015820380ffb823b1b35df64dcd3457.svg
Dong-Bok Lee
user
dongboklee
68de602e70ada21878c75001
Juho Jung
claimed_verified
2025-10-03T12:53:00.709Z
68df412d44f83e2a9773765d
/avatars/3c8a57df03972f57b9b1c6484d3ea7e2.svg
Juho Jung
user
juhojung
68de602e70ada21878c75002
Dongseop Kim
68de602e70ada21878c75003
Won Hwa Kim
68de602e70ada21878c75004
Sunghoon Joo
Automated Structured Radiology Report Generation with Rich Clinical
  Context
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.
Incorporating clinical context into automated structured radiology report generation improves report quality by addressing temporal hallucinations and utilizing comprehensive patient data.
structured radiology report generation chest X-ray images clinical context temporal hallucinations multimodal large language models multi-view X-ray images clinical indication imaging techniques prior studies patient histories