Summary of Rexamine-global: a Framework For Uncovering Inconsistencies in Radiology Report Generation Metrics, by Oishi Banerjee et al.

ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics

by Oishi Banerjee, Agustina Saenz, Kay Wu, Warren Clements, Adil Zia, Dominic Buensalido, Helen Kavnoudias, Alain S. Abi-Ghanem, Nour El Ghawi, Cibele Luna, Patricia Castillo, Khaled Al-Surimi, Rayyan A. Daghistani, Yuh-Min Chen, Heng-sheng Chao, Lars Heiliger, Moon Kim, Johannes Haubold, Frederic Jonske, Pranav Rajpurkar

First submitted to arxiv on: 29 Aug 2024

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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 new framework for evaluating the quality of AI-generated radiology reports is proposed, aiming to address the need for robust metrics that can accurately measure the quality of these reports across diverse hospitals. The ReXamine-Global method uses a large language model (LLM) and tests metrics on different writing styles and patient populations, exposing gaps in their generalization. The framework assesses whether a metric is sensitive to reporting style and reliably agrees with experts or not. Using 240 reports from six hospitals worldwide, the authors applied ReXamine-Global to seven established report evaluation metrics and uncovered serious gaps in their generalizability. This work can guide developers of new report evaluation metrics to ensure robustness across sites and inform users of existing metrics to select reliable evaluation procedures.
Low	GrooveSquid.com (original content)	Low Difficulty Summary AI-generated radiology reports are becoming more common, but it’s hard to tell if they’re good or bad without a way to measure their quality. The problem is that different hospitals might have different ways of writing up patient results, which makes it tricky to compare how well AI models do. A new method called ReXamine-Global tries to solve this problem by testing how well existing metrics work across different hospitals and writing styles. By using 240 reports from six hospitals around the world, researchers found that many current metrics don’t generalize well – they might work great at one hospital but not so much at another. This means that if you’re trying to use these metrics in your own hospital, you might need to adjust them first. The goal is to create better, more reliable metrics for evaluating AI-generated radiology reports.