Summary of Evaluating Automated Radiology Report Quality Through Fine-grained Phrasal Grounding Of Clinical Findings, by Razi Mahmood et al.
Evaluating Automated Radiology Report Quality through Fine-Grained Phrasal Grounding of Clinical Findings
by Razi Mahmood, Pingkun Yan, Diego Machado Reyes, Ge Wang, Mannudeep K. Kalra, Parisa Kaviani, Joy T. Wu, Tanveer Syeda-Mahmood
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 A novel approach for evaluating generative AI reports for chest radiographs is presented in this paper. The method extracts fine-grained finding patterns capturing clinical findings, their location, laterality, and severity, using lexical, semantic, or clinical named entity recognition methods. These patterns are then localized to anatomical regions on chest radiograph images through phrasal grounding. A combined textual and visual evaluation metric is developed, which is compared with other textual metrics on a gold standard dataset derived from the MIMIC collection. The results demonstrate robustness and sensitivity to factual errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to check if AI-generated reports for chest X-rays are accurate. It works by finding specific details about what’s being looked at in the image, such as where something is or how serious it is. This information is then matched with actual locations on the X-ray picture. The quality of these reports is then judged based on both what’s written and what’s shown on the image. The method is tested on a special collection of real chest X-rays and shows that it can detect when AI-generated reports are wrong. |
Keywords
» Artificial intelligence » Grounding » Named entity recognition
