Summary of Semantic Consistency-based Uncertainty Quantification For Factuality in Radiology Report Generation, by Chenyu Wang et al.
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation
by Chenyu Wang, Weichao Zhou, Shantanu Ghosh, Kayhan Batmanghelich, Wenchao Li
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| 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 novel Semantic Consistency-Based Uncertainty Quantification framework is introduced to address the challenge of ensuring factual correctness in radiology report generation. While generative medical Vision Large Language Models (VLLMs) have improved report quality and coherence, they are prone to hallucinations and can produce inaccurate diagnostic information. The proposed method provides both report-level and sentence-level uncertainties without requiring modifications to the underlying model or access to its inner state, making it a plug-and-play module that can be integrated with state-of-the-art models. Extensive experiments demonstrate the efficacy of the method in detecting hallucinations and enhancing factual accuracy, achieving a 10% improvement in factuality scores by rejecting 20% of reports using the Radialog model on the MIMIC-CXR dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make sure radiology reports are accurate. Right now, machines can generate reports, but they sometimes get things wrong. The proposed method adds a check to make sure the report is correct and makes it possible for doctors to review the report before using it. This helps improve the accuracy of the report by 10%. It also flags parts of the report that are most likely to be incorrect. |
