A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports

View a PDF of the paper titled ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports, by Romain Hardy and 2 other authors

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Abstract: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.