ReXTrust：一种用于AI生成放射学报告中细粒度幻觉检测的模型
ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports
AI总结：
针对AI生成放射学报告中的幻觉问题，提出ReXTrust框架，利用大型视觉语言模型的隐藏状态生成发现级幻觉风险评分，在MIMIC-CXR子集上AUROC达0.8751，提升了医疗AI系统的安全性与可靠性。
摘要：
AI生成的放射学报告日益普及，亟需稳健的方法来检测幻觉——即可能影响患者护理的虚假或无根据陈述。我们提出了ReXTrust，一种用于AI生成放射学报告中细粒度幻觉检测的新型框架。我们的方法利用来自大型视觉语言模型的隐藏状态序列，生成发现级别的幻觉风险评分。我们在MIMIC-CXR数据集的子集上评估了ReXTrust，并展现出优于现有方法的性能，在所有发现上达到0.8751的AUROC，在临床显著发现上达到0.8963。结果表明，利用模型隐藏状态的白盒方法可为医疗AI系统提供可靠的幻觉检测，有望提升自动化放射学报告的安全性与可靠性。
英文摘要：
The increasing adoption of AI-generated radiology reports necessitates robust methods for detecting hallucinations--false or unfounded statements that could impact patient care. We present ReXTrust, a novel framework for fine-grained hallucination detection in AI-generated radiology reports. Our approach leverages sequences of hidden states from large vision-language models to produce finding-level hallucination risk scores. We evaluate ReXTrust on a subset of the MIMIC-CXR dataset and demonstrate superior performance compared to existing approaches, achieving an AUROC of 0.8751 across all findings and 0.8963 on clinically significant findings. Our results show that white-box approaches leveraging model hidden states can provide reliable hallucination detection for medical AI systems, potentially improving the safety and reliability of automated radiology reporting.
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