Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation
Bibliographic record
Abstract
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 of structured radiology reports that serves as a gold standard for evaluating structured report frameworks. It is designed to support comprehensive assessment of both single-report interpretation and longitudinal reasoning. Constructed from a subset of the MIMIC-CXR test set, LUNGUAGE comprises 1,473 chest X-ray reports from 230 patients, annotated with over 17,000 expert-verified entities and 23,000 relation-attribute pairs across 18 relation types. An additional subset of 80 sequential reports from 10 patients captures disease progression across 3 to 14 studies per patient, covering time intervals from 1 to 1,200 days. These are annotated with over 41,000 pairwise comparisons, grouped into semantically and temporally coherent groups. The dataset also includes a schema-aligned vocabulary covering diagnostic entities and attributes. All annotations were conducted and verified by board-certified radiologists, resulting in a clinically grounded resource for structured understanding and temporal reasoning in radiology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".