The study evaluated ten chest X‑ray AI classification models on 12 datasets from six countries, focusing on image‑level classification and measuring specificity at 95% sensitivity. MAVL and MedKLIP achieved the highest specificity (≈0.37) but still produced many false positives, with performance dropping on institutional cohorts compared to public benchmarks. No single model excelled across all findings, countries, or lesion types, highlighting limited generalisability.
By Qinmei Xu, Yiheng Li, Wenya Zhao, Zihao Zhao, Chuanjun Xu, Ahmet Gorkem Er, Xianghao Zhan, Brittany Dashevsky, Mohammed Alawad, Marc von der St\"uck, Marvin Gazibari\'c, Daniel Truhn, Shunsuke Shibata, Ryosuke Horino, Mio Ito, Akio Hiwatashi, Suyu Li, Mengya Yang, Ya Liu, Zhiyuan Sun, Changsheng Zhou, Haruka Itakura, Xiao Li, Olivier Gevaert
arXiv:2606. 17710v1 Announce Type: cross Abstract: Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image.
By Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh
Med-AR introduces two autoregressive vision‑language models, Med‑AR‑8B and Med‑AR‑2B, pretrained on structured radiology reports, abnormality‑focused text, and region annotations to address long‑tailed chest X‑ray classification. The models outperform existing contrastive, self‑supervised, and supervised encoders—including Med‑CLIP, CheXFound, EVA‑Base, ARK, and BioViL‑T—across PadChest, MIMIC‑CXR, and CheXpert, achieving higher mean AUROC and AUPRC for head, medium, and tail findings and lower excess area under the risk‑coverage curve. Med‑AR also demonstrates improved selective‑prediction performance, with Med‑AR‑8B raising tail‑label mean AUPRC on MIMIC‑CXR from 0.1033 to 0.1441 and Med‑AR‑2B delivering the strongest discrimination on PadChest.
By Janhavi Prabhu, Sahil, Akshay V, Shivam Shukla, Manoj Tadepalli, Preetham Putha
arXiv:2607. 25589v1 Announce Type: cross Abstract: Medical-imaging AI benchmarks combine datasets, DICOM rendering, prompts, provider APIs, automated labels, statistical code, manuscripts, and repository releases.
By Mateusz Koz{\l}owski
Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical overlap or aggregate clinical correctness. However, such metrics do not test whether individual diagnostic statements stem from the actual pathological evidence visible in the image.
The study introduces GazeRIB, a method that uses radiologists’ gaze data to mask unseen regions during training of chest X‑ray models. By comparing panels of three, five, and eight readers against individual readers across multiple architectures, the authors found that panels consistently improved macro F1 scores by 5.33–7.47 percentage points, with three readers capturing most of the benefit. External validation showed panel‑based models also enhanced ROC‑AUC on the SIIM‑FISABIO‑RSNA dataset, though results varied on other cohorts.
By Nattakorn Kittisut, Roy M. Gabriel, Marly van Assen, Mohammadreza Zandehshahvar, Carlo N. De Cecco, Elizabeth A. Krupinski, Ali Adibi