用大模型生成真实可信的放射科报告错误，助力报告质量提升
ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports
- 基于大模型设计新采样策略，生成多样且符合临床逻辑的错误
- 错误类型覆盖真实场景，与实际报告错误高度一致
- 适合开发纠错算法的研究者和医疗AI质量评估团队使用
准确解读医学影像并撰写放射科报告是医疗中的关键但具挑战性的任务。人工和AI生成的报告均可能包含从临床错误到语言失误的各种问题。为此，我们提出ReXErr，一种利用大语言模型在胸部X光报告中生成代表性错误的方法。通过与持证放射科医生合作，我们定义了涵盖人类和AI报告中常见错误的类别。该方法采用新颖的采样机制，在保持临床合理性的同时注入多样化错误。ReXErr在各类错误中表现出一致性，生成的错误与真实世界场景高度相似。该方法可助力报告纠错算法的研发与评估，有望提升放射科报告的质量与可靠性。
原文摘要 · Abstract (English)
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.
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