# CRG Score: A Distribution-Aware Clinical Metric for Radiology Report Generation

arXiv: https://arxiv.org/abs/2505.17167 · HTML: https://mlsanity.com/paper/2505.17167/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 9 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 9 of 9)

- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography](https://mlsanity.com/paper/2403.17834/) (2024)
- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [GREEN: Generative Radiology Report Evaluation and Error Notation](https://mlsanity.com/paper/2405.03595/) (2024)
- [Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset](https://mlsanity.com/paper/2406.06512/) (2024)
- [CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging](https://mlsanity.com/paper/2403.06801/) (2024)
- [Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes](https://mlsanity.com/paper/2002.04752/) (2020)
- [GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes](https://mlsanity.com/paper/2305.16037/) (2023)

## Built on by (most cited first, 6 of 6)

- [Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imaging](https://mlsanity.com/paper/2510.20639/) (2025)
- [Region-Aware Multimodal Large Language Model via SlowFast Tokenization and Pseudo-Mask Guidance for 3D CT Report Generation](https://mlsanity.com/paper/2506.23102/) (2025)
- [Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans](https://mlsanity.com/paper/2510.10779/) (2025)
- [Enhancing Fine-Grained Spatial Grounding in 3D CT Report Generation via Discriminative Guidance](https://mlsanity.com/paper/2604.10437/) (2026)
- [CTest-Metric: A Unified Framework to Assess Clinical Validity of Metrics for CT Report Generation](https://mlsanity.com/paper/2601.11488/) (2026)
- [NV-Reason-CT: 3D Visual Language Model for CT Analysis](https://mlsanity.com/paper/2609.27511/) (2026)

## Abstract (arXiv)

Evaluating long-context radiology report generation is challenging. NLG metrics fail to capture clinical correctness, while LLM-based metrics often lack generalizability. Clinical accuracy metrics are more relevant but are sensitive to class imbalance, frequently favoring trivial predictions. We propose the CRG Score, a distribution-aware and adaptable metric that evaluates only clinically relevant abnormalities explicitly described in reference reports. CRG supports both binary and structured labels (e.g., type, location) and can be paired with any LLM for feature extraction. By balancing penalties based on label distribution, it enables fairer, more robust evaluation and serves as a clinically aligned reward function.