ReportQA: QA-Based Radiology Report Evaluation
Radiology report evaluation is essential for advancing automated report generation. Natural language generation metrics have limited clinical relevance. Clinical efficacy (CE) metrics evaluate important medical findings, but focus mainly on presence and cover only a limited set of entities. Due to heavy reliance on manual annotations, it is difficult for CE metrics to extend clinical entities or attributes. In clinical practice, radiology reports serve as a medium for information transfer. Clinicians use them to perform downstream diagnostic tasks without directly inspecting images. Based on this insight, we propose ReportQA, a clinical-related and flexible radiology report evaluation fra...
| Authors | Yiming Shi, Shaoshuai Yang, Xi Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
Popularity, last 4 days
0 now. Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- gottoknow/ReportQADataset · cited by
- shiym2000/ReportQADataset · cited by
Models and datasets referencing this paper
- shiym2000/ReportQADataset
- gottoknow/ReportQADataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 20 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.