@inproceedings{jiang-etal-2025-clear,
    title = "{CLEAR}: A Clinically Grounded Tabular Framework for Radiology Report Evaluation",
    author = "Jiang, Yuyang  and
      Chen, Chacha  and
      Wang, Shengyuan  and
      Li, Feng  and
      Tang, Zecong  and
      Mervak, Benjamin M.  and
      Chelala, Lydia  and
      Straus, Christopher M  and
      Chahine, Reve  and
      Iii, Samuel G. Armato  and
      Tan, Chenhao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.862/",
    doi = "10.18653/v1/2025.findings-emnlp.862",
    pages = "15914--15933",
    ISBN = "979-8-89176-335-7",
    abstract = "Existing metrics often lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports, resulting in suboptimal evaluation. We introduce a **Cl**inically grounded tabular framework with **E**xpert-curated labels and **A**ttribute-level comparison for **R**adiology report evaluation (**CLEAR**). CLEAR not only examines whether a report can accurately identify the presence or absence of medical conditions, but it also assesses whether the report can precisely describe each positively identified condition across five key attributes: first occurrence, change, severity, descriptive location, and recommendation. Compared with prior works, CLEAR{'}s multi-dimensional, attribute-level outputs enable a more comprehensive and clinically interpretable evaluation of report quality. Additionally, to measure the clinical alignment of CLEAR, we collaborated with five board-certified radiologists to develop **CLEAR-Bench**, a dataset of 100 chest radiograph reports from MIMIC-CXR, annotated across 6 curated attributes and 13 CheXpert conditions. Our experiments demonstrated that CLEAR achieves high accuracy in extracting clinical attributes and provides automated metrics that are strongly aligned with clinical judgment."
}