RADAR: A Multimodal Benchmark for 3D Image-Based Radiology Report Review
Zhaoyi Sun, Minal Jagtiani, Wen-wai Yim, Fei Xia, Martin Gunn, Meliha Yetisgen, Asma Ben Abacha

TL;DR
RADAR introduces a comprehensive multimodal benchmark dataset for analyzing discrepancies in radiology reports by pairing 3D medical images with reports and edits, facilitating the development of models that support clinical review and quality assurance.
Contribution
This work presents RADAR, a novel benchmark dataset with structured discrepancy assessment tasks for multimodal models in radiology report review, emphasizing clinical reasoning and image-text alignment.
Findings
Provides expert-annotated abdominal CT dataset
Defines structured tasks for discrepancy evaluation
Supports systematic comparison of multimodal review models
Abstract
Radiology reports for the same patient examination may contain clinically meaningful discrepancies arising from interpretation differences, reporting variability, or evolving assessments. Systematic analysis of such discrepancies is important for quality assurance, clinical decision support, and multimodal model development, yet remains limited by the lack of standardized benchmarks. We present RADAR, a multimodal benchmark for radiology report discrepancy analysis that pairs 3D medical images with a preliminary report and corresponding candidate edits for the same study. The dataset reflects a standard clinical workflow in which trainee radiologists author preliminary reports that are subsequently reviewed and revised by attending radiologists. RADAR defines a structured discrepancy assessment task requiring models to evaluate proposed edits by determining image-level agreement,…
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Taxonomy
TopicsRadiology practices and education · Radiomics and Machine Learning in Medical Imaging · Artificial Intelligence in Healthcare and Education
