# ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports

arXiv: https://arxiv.org/abs/2409.10829 · HTML: https://mlsanity.com/paper/2409.10829/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023)
- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [Interactive and Explainable Region-guided Radiology Report Generation](https://mlsanity.com/paper/2304.08295/) (2023)
- [MAIRA-1: A specialised large multimodal model for radiology report generation](https://mlsanity.com/paper/2311.13668/) (2023)
- [ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders](https://mlsanity.com/paper/2308.01317/) (2023)
- [Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors](https://mlsanity.com/paper/2210.06340/) (2022)
- [Automated Radiology Report Generation: A Review of Recent Advances](https://mlsanity.com/paper/2405.10842/) (2024)
- [MedVersa: A Generalist Foundation Model for Medical Image Interpretation](https://mlsanity.com/paper/2405.07988/) (2024)
- [A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images](https://mlsanity.com/paper/2010.10563/) (2020)
- [ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs](https://mlsanity.com/paper/2305.15964/) (2023)
- [Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis](https://mlsanity.com/paper/2303.11224/) (2023)
- [FineRadScore: A Radiology Report Line-by-Line Evaluation Technique Generating Corrections with Severity Scores](https://mlsanity.com/paper/2405.20613/) (2024)
- [Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images](https://mlsanity.com/paper/2310.07027/) (2023)
- [Generating Synthetic X-ray Images of a Person from the Surface Geometry](https://mlsanity.com/paper/1805.00553/) (2018)
- [Customizing General-Purpose Foundation Models for Medical Report Generation](https://mlsanity.com/paper/2306.05642/) (2023)
- [Direct Preference Optimization for Suppressing Hallucinated Prior Exams in Radiology Report Generation](https://mlsanity.com/paper/2406.06496/) (2024)

## Built on by (most cited first, 9 of 9)

- [Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation](https://mlsanity.com/paper/2505.21190/) (2025)
- [MedVAL: Toward Expert-Level Medical Text Validation with Language Models](https://mlsanity.com/paper/2507.03152/) (2025)
- [Vision-language models for chest radiography do not always need the image](https://mlsanity.com/paper/2606.17710/) (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)
- [OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation](https://mlsanity.com/paper/2509.18600/) (2025)
- [RADAR: A Multimodal Benchmark for 3D Image-Based Radiology Report Review](https://mlsanity.com/paper/2603.06681/) (2026)
- [RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation](https://mlsanity.com/paper/2606.08769/) (2026)
- [Symbal: Detecting Systematic Misalignments in Model-Generated Captions](https://mlsanity.com/paper/2607.15216/) (2026)

## Abstract (arXiv)

Accurately interpreting medical images and writing radiology reports is a critical but challenging task in healthcare. Both human-written and AI-generated reports can contain errors, ranging from clinical inaccuracies to linguistic mistakes. To address this, we introduce ReXErr, a methodology that leverages Large Language Models to generate representative errors within chest X-ray reports. Working with board-certified radiologists, we developed error categories that capture common mistakes in both human and AI-generated reports. Our approach uses a novel sampling scheme to inject diverse errors while maintaining clinical plausibility. ReXErr demonstrates consistency across error categories and produces errors that closely mimic those found in real-world scenarios. This method has the potential to aid in the development and evaluation of report correction algorithms, potentially enhancing the quality and reliability of radiology reporting.