# ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation

arXiv: https://arxiv.org/abs/2411.15122 · HTML: https://mlsanity.com/paper/2411.15122/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 22 Nov 2024.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [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)
- [BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2305.17100/) (2023)
- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [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)
- [Improving Chest X-Ray Report Generation by Leveraging Warm Starting](https://mlsanity.com/paper/2201.09405/) (2022)
- [Chest ImaGenome Dataset for Clinical Reasoning](https://mlsanity.com/paper/2108.00316/) (2021)
- [RaTEScore: A Metric for Radiology Report Generation](https://mlsanity.com/paper/2406.16845/) (2024)
- [LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation](https://mlsanity.com/paper/2305.11490/) (2023)
- [FineRadScore: A Radiology Report Line-by-Line Evaluation Technique Generating Corrections with Severity Scores](https://mlsanity.com/paper/2405.20613/) (2024)

## Built on by (most cited first, 25 of 25)

- [Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning](https://mlsanity.com/paper/2506.07044/) (2025)
- [ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports](https://mlsanity.com/paper/2505.00228/) (2025)
- [RadVLM: A Multitask Conversational Vision-Language Model for Radiology](https://mlsanity.com/paper/2502.03333/) (2025)
- [ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports](https://mlsanity.com/paper/2507.22030/) (2025)
- [Scaling medical imaging report generation with multimodal reinforcement learning](https://mlsanity.com/paper/2601.17151/) (2026)
- [DDaTR: Dynamic Difference-aware Temporal Residual Network for Longitudinal Radiology Report Generation](https://mlsanity.com/paper/2505.03401/) (2025)
- [From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine](https://mlsanity.com/paper/2502.09242/) (2025)
- [CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs](https://mlsanity.com/paper/2505.10496/) (2025)
- [A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation](https://mlsanity.com/paper/2604.00493/) (2026)
- [LiveClin: A Live Clinical Benchmark without Leakage](https://mlsanity.com/paper/2602.16747/) (2026)
- [Discrete Diffusion Language Models for Interactive Radiology Report Drafting](https://mlsanity.com/paper/2607.01436/) (2026)
- [CorBenchX: Large-Scale Chest X-Ray Error Dataset and Vision-Language Model Benchmark for Report Error Correction](https://mlsanity.com/paper/2505.12057/) (2025)
- [Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays](https://mlsanity.com/paper/2509.15234/) (2025)
- [Random Direct Preference Optimization for Radiography Report Generation](https://mlsanity.com/paper/2509.21351/) (2025)
- [MI-CXR: A Benchmark for Longitudinal Reasoning over Multi-Interval Chest X-rays](https://mlsanity.com/paper/2605.15574/) (2026)
- [RadHiera: Semantic Hierarchical Reinforcement Learning for Medical Report Generation](https://mlsanity.com/paper/2511.10065/) (2025)
- [Benchmark Success, Clinical Failure: When Reinforcement Learning Optimizes for Benchmarks, Not Patients](https://mlsanity.com/paper/2512.23090/) (2025)
- [Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation](https://mlsanity.com/paper/2601.16753/) (2026)
- [Ran Score: a LLM-based Evaluation Score for Radiology Report Generation](https://mlsanity.com/paper/2603.22935/) (2026)
- [ReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding](https://mlsanity.com/paper/2604.10916/) (2026)
- [CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs](https://mlsanity.com/paper/2605.05810/) (2026)
- [SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generation](https://mlsanity.com/paper/2606.30201/) (2026)
- [CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement](https://mlsanity.com/paper/2608.03890/) (2026)
- [Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation](https://mlsanity.com/paper/2609.19093/) (2026)
- [BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning](https://mlsanity.com/paper/2610.06571/) (2026)

## Abstract (arXiv)

AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https://rexrank.ai, a public leaderboard and challenge for assessing AI-powered radiology report generation. Our framework incorporates ReXGradient, the largest test dataset consisting of 10,000 studies, and three public datasets (MIMIC-CXR, IU-Xray, CheXpert Plus) for report generation assessment. ReXrank employs 8 evaluation metrics and separately assesses models capable of generating only findings sections and those providing both findings and impressions sections. By providing this standardized evaluation framework, ReXrank enables meaningful comparisons of model performance and offers crucial insights into their robustness across diverse clinical settings. Beyond its current focus on chest X-rays, ReXrank's framework sets the stage for comprehensive evaluation of automated reporting across the full spectrum of medical imaging.