Lunguage: A Benchmark for Structured and Sequential Chest X-ray
  Interpretation
Authors: name: Jong Hak Moon, name: Geon Choi, name: Paloma Rabaey, name: Min Gwan Kim, name: Hyuk Gi Hong, name: Jung-Oh Lee, name: Hangyul Yoon, name: Eun Woo Doe, name: Jiyoun Kim, name: Harshita Sharma, name: Daniel C. Castro, name: Javier Alvarez-Valle, name: Edward Choi
Organization: KAIST AI

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