ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929501496147968 |
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| author | Rao, Vishwanatha M. Zhang, Serena Acosta, Julian N. Adithan, Subathra Rajpurkar, Pranav |
| author_facet | Rao, Vishwanatha M. Zhang, Serena Acosta, Julian N. Adithan, Subathra Rajpurkar, Pranav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10829 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports Rao, Vishwanatha M. Zhang, Serena Acosta, Julian N. Adithan, Subathra Rajpurkar, Pranav Computation and Language 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. |
| title | ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.10829 |