Summary of Rextrust: a Model For Fine-grained Hallucination Detection in Ai-generated Radiology Reports, by Romain Hardy et al.
ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports
by Romain Hardy, Sung Eun Kim, Du Hyun Ro, Pranav Rajpurkar
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents ReXTrust, a novel framework for fine-grained hallucination detection in AI-generated radiology reports. The increasing adoption of AI-generated reports necessitates robust methods to detect false or unfounded statements that could impact patient care. The approach leverages sequences of hidden states from large vision-language models to produce finding-level hallucination risk scores. Evaluations on a subset of the MIMIC-CXR dataset demonstrate superior performance compared to existing approaches, achieving an AUROC of 0.8751 across all findings and 0.8963 on clinically significant findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI-generated radiology reports are becoming increasingly common, but they can contain false or unfounded statements that could impact patient care. Researchers have developed a new way to detect these mistakes, called ReXTrust. This method uses special hidden states from large computer models to figure out which parts of the report might be incorrect. Tests on real medical data show that this approach is better than others at finding these mistakes, and it could help make AI-generated reports safer and more reliable. |
Keywords
» Artificial intelligence » Hallucination
