ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports
<|reference_start|>ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports: 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.<|reference_end|>
@article{rao2024rexerr:,
    title={ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic
  Radiology Reports},
    author={Vishwanatha M. Rao, Serena Zhang, Julian N. Acosta, Subathra Adithan,
  Pranav Rajpurkar},
    journal={arXiv preprint arXiv:2409.10829},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10829},
    primaryClass={cs.CL}
}