Evaluating Automated Radiology Report Quality through Fine-Grained Phrasal Grounding of Clinical Findings

While some metrics cover clinical entities and their relations[9, 11], generally Several evaluation metrics have been developed recently to scoring metrics do not explicitly capture the textual mention automatically assess the quality of generative AI reports for differences in the anatomy, laterality and severity. Further, chest radiographs based only on textual information using phrasal grounding of the findings in terms of anatomical localization lexical, semantic, or clinical named entity recognition methods. in images is not exploited in the quality scoring. In this paper, we develop a new method of report quality In this paper, we propose a metric that captures both finegrained evaluation by first extracting fine-grained finding patterns textual descriptions of findings as well as their phrasal capturing the location, laterality, and severity of a large number grounding information in terms of anatomical locations in images. of clinical findings. We then performed phrasal grounding We present results that compare this evaluation metric to localize their associated anatomical regions on chest radiograph to other textual metrics on a gold standard dataset derived images. The textual and visual measures are then combined from MIMIC collection of chest X-rays and validated reports, to rate the quality of the generated reports. We present to show its robustness and sensitivity to factual errors.