ReXErr-v1: Clinically Meaningful Chest X-Ray Report Errors Derived from MIMIC-CXR

Interpreting medical images and writing radiology reports is a critical yet challenging task in healthcare. Despite their importance, both human-written and AI-generated reports are liable to errors, leaving a need for robust and representative datasets that capture the diversity of errors present across different mediums of report generation. Thus, we present Chest X-Ray Report Errors (ReXErr-v1), a new dataset based on MIMIC-CXR and constructed using large language models (LLMs) that contains synthetic error reports for the majority of MIMIC-CXR. Developed with input from board-certified radiologists, ReXErr-v1 contains plausible errors that closely mimic those found in real- world scenarios. Furthermore, ReXErr-v1 utilizes a novel sampling methodology that selects three errors to inject among a set of frequent errors made by both human and AI models. We include errors both at report and sentence level, improving the versatility of ReXErr-v1. Our dataset can enhance future AI reporting tools by aiding the development and evaluation of report-generation and error-screening algorithms.