# RadMatch: Auditable Radiology Report Evaluation via Finding-Level Matching

arXiv: https://arxiv.org/abs/2609.01470 · HTML: https://mlsanity.com/paper/2609.01470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 92 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 29 of 29)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography](https://mlsanity.com/paper/2403.17834/) (2024)
- [MAIRA-2: Grounded Radiology Report Generation](https://mlsanity.com/paper/2406.04449/) (2024)
- [Interactive and Explainable Region-guided Radiology Report Generation](https://mlsanity.com/paper/2304.08295/) (2023)
- [GREEN: Generative Radiology Report Evaluation and Error Notation](https://mlsanity.com/paper/2405.03595/) (2024)
- [MAIRA-1: A specialised large multimodal model for radiology report generation](https://mlsanity.com/paper/2311.13668/) (2023)
- [RaTEScore: A Metric for Radiology Report Generation](https://mlsanity.com/paper/2406.16845/) (2024)
- [XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models](https://mlsanity.com/paper/2306.07971/) (2023)
- [Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation](https://mlsanity.com/paper/2403.08002/) (2024)
- [Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology](https://mlsanity.com/paper/2402.14252/) (2024)
- [MedGemma 1.5 Technical Report](https://mlsanity.com/paper/2604.05081/) (2026)
- [Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation](https://mlsanity.com/paper/2407.02235/) (2024)
- [Large Language Model with Region-guided Referring and Grounding for CT Report Generation](https://mlsanity.com/paper/2411.15539/) (2024)
- [Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation](https://mlsanity.com/paper/2407.15268/) (2024)
- [RadEval: A framework for radiology text evaluation](https://mlsanity.com/paper/2509.18030/) (2025)
- [FineRadScore: A Radiology Report Line-by-Line Evaluation Technique Generating Corrections with Severity Scores](https://mlsanity.com/paper/2405.20613/) (2024)
- [LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation](https://mlsanity.com/paper/2404.00998/) (2024)
- [DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation](https://mlsanity.com/paper/2504.11786/) (2025)
- [CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation](https://mlsanity.com/paper/2603.06183/) (2026)
- [MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models](https://mlsanity.com/paper/2411.11362/) (2024)
- [CWCD: Category-Wise Contrastive Decoding for Structured Medical Report Generation](https://mlsanity.com/paper/2604.10410/) (2026)
- [Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation](https://mlsanity.com/paper/2605.30984/) (2026)
- [HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation](https://mlsanity.com/paper/2409.13038/) (2024)
- [RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores](https://mlsanity.com/paper/2508.15464/) (2025)
- [Automated Structured Radiology Report Generation with Rich Clinical Context](https://mlsanity.com/paper/2510.00428/) (2025)
- [CTest-Metric: A Unified Framework to Assess Clinical Validity of Metrics for CT Report Generation](https://mlsanity.com/paper/2601.11488/) (2026)

## Built on by (most cited first, 1 of 1)

- [Can Jev Judge Radiology Reports? Evaluating a System One Model for Clinical Factuality](https://mlsanity.com/paper/2609.27607/) (2026)

## Abstract (arXiv)

As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language model (LLM)-based metrics are now the best-correlated with radiologist judgment, yet they output a single opaque score that neither a clinician nor a model builder can easily interpret or audit. We introduce RadMatch, a multi-stage, LLM-based metric that decomposes report comparison into a structured finding-level matching with significance-aware scoring and error characterization across seven clinical attribute dimensions (status, location, severity, morphology, certainty, longitudinal comparison, and measurement). The main score is the actionable-error count, both interpretable and auditable. Candidate findings are graded correct, partial, or incorrect, and unmatched findings are counted as missed or hallucinated. Triage and actionable safety recall/precision and per-subset views add complementary, deployment-oriented lenses. Across two expert benchmarks, RadMatch is the most clinically aligned metric, matching inter-radiologist agreement on ReXVal and more than doubling the best prior metric on the harder RadEvalExpert. Relying only on few-shot prompting, it is designed to extend to other modalities and anatomies. We will release RadMatch as open-source code with an interactive dashboard for inspecting results.