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.