arXiv AI By Jiaju Huang, Hao Yang, Xinyu Ma, Xinglong Liang, Kunyan Cai, Junqiang Ma, Shaobin Chen, Yue Sun, Tao Tan

Can Jev Judge Radiology Reports? Evaluating a System One Model for Clinical Factuality

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arXiv AI
Jun 9

RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation

arXiv:2606. 08769v1 Announce Type: cross Abstract: Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertainty mismatches, and temporal-comparison errors rather than low surface similarity alone.

By Weixin Liu, Juming Xiong, Yang Li, Qingyuan Song, Susannah Rose, Murat Kantarcioglu, Bradley Malin, Zhijun Yin
arXiv AI
Sep 17

Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation

The study examines how differences in radiologists’ reporting styles—such as terminology, shorthand, formatting, and detail—affect the evaluation of AI-generated chest X‑ray reports. By quantifying the sensitivity of common metrics to these variations, the authors show that changes in reference reports can shift model rankings. They introduce a taxonomy of reporting variations and a rewriting method, ReRef, that preserves clinical meaning while altering style, and release a validated dataset of paired reference reports to aid future research.

By Daniel P. Jeong, Charles Q. Li, Hossein Hosseiny, Nitya M. Bhalla, Fatma Uyar Morency, Pradeep Ravikumar, Zachary C. Lipton, Michael Oberst
arXiv AI
Jun 15

Can LLMs Accurately Score Medical Diagnoses and Clinical Reasoning?

arXiv:2604. 14892v3 Announce Type: replace-cross Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudicators.

By Amy Rouillard, Sitwala Mundia, Linda Camara, Ziyaad Dangor, Michael Cameron Gramanie, Ismail Kalla, Shabir A. Madhi, Kajal Morar, Marlvin T. Ncube, Haroon Saloojee, Bruce A. Bassett