# ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports

arXiv: https://arxiv.org/abs/2505.00228 · HTML: https://mlsanity.com/paper/2505.00228/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 4 of 4)

- [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)
- [MedVersa: A Generalist Foundation Model for Medical Image Interpretation](https://mlsanity.com/paper/2405.07988/) (2024)
- [ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation](https://mlsanity.com/paper/2411.15122/) (2024)

## Built on by (most cited first, 27 of 27)

- [RadEval: A framework for radiology text evaluation](https://mlsanity.com/paper/2509.18030/) (2025)
- [ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding](https://mlsanity.com/paper/2506.04353/) (2025)
- [Scaling medical imaging report generation with multimodal reinforcement learning](https://mlsanity.com/paper/2601.17151/) (2026)
- [CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation](https://mlsanity.com/paper/2603.06183/) (2026)
- [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)
- [EditGRPO: Reinforcement Learning with Post-Rollout Edits for Clinically Accurate Chest X-Ray Report Generation](https://mlsanity.com/paper/2509.22812/) (2025)
- [ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion](https://mlsanity.com/paper/2604.09450/) (2026)
- [SDR: Set-Distance Rewards for Radiology Report Generation](https://mlsanity.com/paper/2606.00440/) (2026)
- [MedTutor: A Retrieval-Augmented LLM System for Case-Based Medical Education](https://mlsanity.com/paper/2601.06979/) (2026)
- [A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling](https://mlsanity.com/paper/2602.22843/) (2026)
- [Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers](https://mlsanity.com/paper/2606.19460/) (2026)
- [RadGame: An AI-Powered Platform for Radiology Education](https://mlsanity.com/paper/2509.13270/) (2025)
- [Temporal Inversion for Learning Interval Change in Chest X-Rays](https://mlsanity.com/paper/2604.04563/) (2026)
- [SemEnrich: Self-Supervised Semantic Enrichment of Radiology Reports for Vision-Language Learning](https://mlsanity.com/paper/2604.09887/) (2026)
- [CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography](https://mlsanity.com/paper/2605.11304/) (2026)
- [RadHiera: Semantic Hierarchical Reinforcement Learning for Medical Report Generation](https://mlsanity.com/paper/2511.10065/) (2025)
- [Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation](https://mlsanity.com/paper/2603.01625/) (2026)
- [CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs](https://mlsanity.com/paper/2605.05810/) (2026)
- [Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports](https://mlsanity.com/paper/2606.28393/) (2026)
- [Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context](https://mlsanity.com/paper/2607.05880/) (2026)
- [Prospective clinical indication, post-hoc report leakage, and fusion design in multi-image chest radiograph classification: a patient-clustered evaluation](https://mlsanity.com/paper/2607.13800/) (2026)
- [RadHarmony: Radiological Data Handling in the Era of Agentic AI](https://mlsanity.com/paper/2607.27235/) (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)
- [CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation](https://mlsanity.com/paper/2610.13059/) (2026)
- [Intent-Triggered Anchoring for Multi-Token Format Units in Parallel Decoding of Diffusion Language Models](https://mlsanity.com/paper/2610.16516/) (2026)

## Abstract (arXiv)

We present ReXGradient-160K, representing the largest publicly available chest X-ray dataset to date in terms of the number of patients. This dataset contains 160,000 chest X-ray studies with paired radiological reports from 109,487 unique patients across 3 U.S. health systems (79 medical sites). This comprehensive dataset includes multiple images per study and detailed radiology reports, making it particularly valuable for the development and evaluation of AI systems for medical imaging and automated report generation models. The dataset is divided into training (140,000 studies), validation (10,000 studies), and public test (10,000 studies) sets, with an additional private test set (10,000 studies) reserved for model evaluation on the ReXrank benchmark. By providing this extensive dataset, we aim to accelerate research in medical imaging AI and advance the state-of-the-art in automated radiological analysis. Our dataset will be open-sourced at https://huggingface.co/datasets/rajpurkarlab/ReXGradient-160K.