mlsanityData to 14 Oct 2026

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

arXiv 2412.15264 ↗First posted 17 Dec 2024cs.CL cs.AIcited by 12

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 7.

12 papers.

  1. 2025Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalitiescited by 7
  2. 2025UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Modelscited by 5
  3. 2025MedVAL: Toward Expert-Level Medical Text Validation with Language Modelscited by 3
  4. 2025Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reportscited by 2
  5. 2026V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answeringcited by 2
  6. 2025Process Reward Models for Sentence-Level Verification of LVLM Radiology Reportscited by 1
  7. 2026From Retinal Evidence to Safe Decisions: RETINA-SAFE and ECRT for Hallucination Risk Triage in Medical LLMscited by 0
  8. 2026A Benchmark for Hallucination Detection in VLMs for Gastrointestinal Endoscopycited by 0
  9. 2026Detecting Clinical Hallucinations in LVLMs via Counterfactual Visual Grounding Uncertaintycited by 0
  10. 2026Symbal: Detecting Systematic Misalignments in Model-Generated Captionscited by 0
  11. 2026Auditing Medical Vision-Language Models on Chest Radiographs: Estimating Reference Agreement Across Institutionscited by 0
  12. 2026Unmentioned Checklist Findings Change How Reinforcement Learning Appears to Improve Chest Radiograph Report Checkingcited by 0

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  1. 2026HalluCXR: Benchmarking and Mitigating Hallucinations in Medical Vision-Language Models for Chest Radiograph Interpretation
  2. 2024RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models
  3. 2026Hallucination in Medical Imaging AI: A Cross-Modality Analytical Framework for Taxonomy, Detection, and Mitigation under Regulatory Constraints
  4. 2026Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
  5. 2026Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain
  6. 2024Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
  7. 2025From Detection to Diagnosis: Advancing Hallucination Analysis with Automated Data Synthesis
  8. 2026CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification