title: Lunguage: A Benchmark for Structured and Sequential Chest X-ray
    Interpretation
summary: Radiology reports convey detailed clinical observations and capture
  diagnostic reasoning that evolves over time. However, existing evaluation
  methods are limited to single-report settings and rely on coarse metrics that
  fail to capture fine-grained clinical semantics and temporal dependencies. We
  introduce LUNGUAGE,a benchmark dataset for structured radiology report
  generation that supports both single-report evaluation and longitudinal
  patient-level assessment across multiple studies. It contains 1,473 annotated
  chest X-ray reports, each reviewed by experts, and 80 of them contain
  longitudinal annotations to capture disease progression and inter-study
  intervals, also reviewed by experts. Using this benchmark, we develop a
  two-stage framework that transforms generated reports into fine-grained,
  schema-aligned structured representations, enabling longitudinal
  interpretation. We also propose LUNGUAGESCORE, an interpretable metric that
  compares structured outputs at the entity, relation, and attribute level while
  modeling temporal consistency across patient timelines. These contributions
  establish the first benchmark dataset, structuring framework, and evaluation
  metric for sequential radiology reporting, with empirical results demonstrating
  that LUNGUAGESCORE effectively supports structured report evaluation. The code
  is available at: https://github.com/SuperSupermoon/Lunguage
paper_authors._id: 6838be453538dad22f9bc2a4
paper_authors.name: Jong Hak Moon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:58:33.487Z
paper_authors._id: 63e50836c9b2b7e1ea3413fd
paper_authors.avatarUrl: /avatars/6c43fbce3ac2564e7860ff33fe8c9e82.svg
paper_authors.fullname: Jonghak Moon
paper_authors.type: user
paper_authors.user: SuperSupermoon
paper_authors._id: 6838be453538dad22f9bc2a5
paper_authors.name: Geon Choi
paper_authors._id: 6838be453538dad22f9bc2a6
paper_authors.name: Paloma Rabaey
paper_authors._id: 6838be453538dad22f9bc2a7
paper_authors.name: Min Gwan Kim
paper_authors._id: 6838be453538dad22f9bc2a8
paper_authors.name: Hyuk Gi Hong
paper_authors._id: 6838be453538dad22f9bc2a9
paper_authors.name: Jung-Oh Lee
paper_authors._id: 6838be453538dad22f9bc2aa
paper_authors.name: Hangyul Yoon
paper_authors._id: 6838be453538dad22f9bc2ab
paper_authors.name: Eun Woo Doe
paper_authors._id: 6838be453538dad22f9bc2ac
paper_authors.name: Jiyoun Kim
paper_authors._id: 6838be453538dad22f9bc2ad
paper_authors.name: Harshita Sharma
paper_authors._id: 6838be453538dad22f9bc2ae
paper_authors.name: Daniel C. Castro
paper_authors._id: 6838be453538dad22f9bc2af
paper_authors.name: Javier Alvarez-Valle
paper_authors._id: 6838be453538dad22f9bc2b0
paper_authors.name: Edward Choi
paper_title: Lunguage: A Benchmark for Structured and Sequential Chest X-ray
    Interpretation
paper_summary: Radiology reports convey detailed clinical observations and capture
  diagnostic reasoning that evolves over time. However, existing evaluation
  methods are limited to single-report settings and rely on coarse metrics that
  fail to capture fine-grained clinical semantics and temporal dependencies. We
  introduce LUNGUAGE,a benchmark dataset for structured radiology report
  generation that supports both single-report evaluation and longitudinal
  patient-level assessment across multiple studies. It contains 1,473 annotated
  chest X-ray reports, each reviewed by experts, and 80 of them contain
  longitudinal annotations to capture disease progression and inter-study
  intervals, also reviewed by experts. Using this benchmark, we develop a
  two-stage framework that transforms generated reports into fine-grained,
  schema-aligned structured representations, enabling longitudinal
  interpretation. We also propose LUNGUAGESCORE, an interpretable metric that
  compares structured outputs at the entity, relation, and attribute level while
  modeling temporal consistency across patient timelines. These contributions
  establish the first benchmark dataset, structuring framework, and evaluation
  metric for sequential radiology reporting, with empirical results demonstrating
  that LUNGUAGESCORE effectively supports structured report evaluation. The code
  is available at: https://github.com/SuperSupermoon/Lunguage
paper_ai_summary: The paper introduces LUNGUAGE, a benchmark for structured radiology report generation, and LUNGUAGESCORE, an evaluation metric, enabling fine-grained structured report evaluation and longitudinal interpretation.
paper_ai_keywords: LUNGUAGE LUNGUAGESCORE structured radiology report generation two-stage framework schema-aligned structured representations evaluative metric entity relation attribute temporal consistency longitudinal interpretation