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
The paper evaluates large language model (LLM) judges for radiology report assessment across multiple imaging modalities and anatomical regions, comparing four existing metrics (RadFact, GREEN, FineRadScore, CRIMSON) with a new metric called VERT. GREEN emerges as the most robust among current metrics, while VERT improves correlation with radiologist judgments by up to 26% over GREEN. The study also shows that ensembling and lightweight fine‑tuning of the Qwen3‑30B model can yield additional performance gains and significant latency reductions.
By Federica Bologna, Jean-Philippe Corbeil, Matthew Wilkens, Asma Ben Abacha
arXiv:2411. 15122v2 Announce Type: replace-cross Abstract: AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays.
By Xiaoman Zhang, Hong-Yu Zhou, Xiaoli Yang, Oishi Banerjee, Juli\'an N. Acosta, Mohammed Baharoon, Josh Miller, Ouwen Huang, Pranav Rajpurkar
arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.
By Panagiotis Fytas, Ian Selby, Clemens Karner, Judith Babar, Simon Baker, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, John Li Chen, Alex Sawer, Michael Roberts, Jonathan Weir-McCall, J. H. F. Rudd, Carola-Bibiane Sch\"onlieb, Anna Korhonen, Anna Breger
arXiv:2609.27607v1 Announce Type: cross
Abstract: An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its pre...
By Jiaju Huang, Hao Yang, Xinyu Ma, Xinglong Liang, Kunyan Cai, Junqiang Ma, Shaobin Chen, Yue Sun, Tao Tan
arXiv:2608. 10505v1 Announce Type: new Abstract: Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content.
By Ying Jin, Noel C. F. Codella, John Corring, Mu Wei, Dinei Florencio, Eric Horvitz
arXiv:2610.07843v1 Announce Type: cross
Abstract: In digital pathology, compact evidence is often used to explain or audit predictions made by whole-slide image multiple instance learning models. In...
By Hyun Do Jung, Jungwon Choi, Soojung Choi, Yujin Oh, Hwiyoung Kim
Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content. Such control is essential because clinical scenarios diverge: emergency triage prioritizes sensitivity to reduce missed findings, whereas confirmatory interpretation emphasizes specificity to limit unnecessary interventions.
arXiv:2607. 18999v1 Announce Type: cross Abstract: Multi-turn medical consultation agents must decide what to ask, adapt to patient responses, and determine when the collected evidence is sufficient.
By Guofeng Zhang, Yizeng Quan, Huaiyi Fang, Jianwei Lv, Jinyao Liu, Xunxu Duan, Lening An, Yu Ouyang, Junfeng Wang
arXiv:2609.22281v1 Announce Type: new
Abstract: Foundation models have recently demonstrated strong capabilities across a wide range of medical imaging tasks. However, their performance in structured...
By Benjamin Renoust, Pierre Baudot, Tiffany Foriel, Yousra Haddou, Charles Voyton, Pierre-Henri Siot, Ezequiel Geremia, Danny Francis, Jean-Christophe Brisset, Val\'erie Bourd\`es, Sylvain Bodard, Benoit Huet
arXiv:2609.01470v1 Announce Type: new
Abstract: As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language...
By Charles Corbi\`ere, L\'eo Machado, Aubin Charley, Baptiste Callard, Pierre Manceron, Corentin Dancette
arXiv:2610.08813v1 Announce Type: new
Abstract: X-ray image-based Radiology Report Generation (RRG) constitutes a critical research direction within medical artificial intelligence, with great potent...
By Xiao Wang, Yuxiang Zhang, Dan Xu, Yuehang Li, Shiao Wang, Bo Jiang, Yaowei Wang, Yonghong Tian, Jin Tang