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

arXiv: https://arxiv.org/abs/2412.15264 · HTML: https://mlsanity.com/paper/2412.15264/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2024.

## Built on by (most cited first, 12 of 12)

- [Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities](https://mlsanity.com/paper/2508.07031/) (2025)
- [UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Models](https://mlsanity.com/paper/2503.20504/) (2025)
- [MedVAL: Toward Expert-Level Medical Text Validation with Language Models](https://mlsanity.com/paper/2507.03152/) (2025)
- [Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports](https://mlsanity.com/paper/2509.21356/) (2025)
- [V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answering](https://mlsanity.com/paper/2601.18240/) (2026)
- [Process Reward Models for Sentence-Level Verification of LVLM Radiology Reports](https://mlsanity.com/paper/2510.23217/) (2025)
- [From Retinal Evidence to Safe Decisions: RETINA-SAFE and ECRT for Hallucination Risk Triage in Medical LLMs](https://mlsanity.com/paper/2604.05348/) (2026)
- [A Benchmark for Hallucination Detection in VLMs for Gastrointestinal Endoscopy](https://mlsanity.com/paper/2606.24115/) (2026)
- [Detecting Clinical Hallucinations in LVLMs via Counterfactual Visual Grounding Uncertainty](https://mlsanity.com/paper/2606.28520/) (2026)
- [Symbal: Detecting Systematic Misalignments in Model-Generated Captions](https://mlsanity.com/paper/2607.15216/) (2026)
- [Auditing Medical Vision-Language Models on Chest Radiographs: Estimating Reference Agreement Across Institutions](https://mlsanity.com/paper/2608.07550/) (2026)
- [Unmentioned Checklist Findings Change How Reinforcement Learning Appears to Improve Chest Radiograph Report Checking](https://mlsanity.com/paper/2610.05425/) (2026)

## Abstract (arXiv)

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.