The paper presents a two‑stage multimodal framework for chest X‑ray interpretation that incorporates radiologist gaze data from the MIMIC‑Eye dataset. Stage 1 introduces a gaze‑token classifier that fuses image patches, bounding‑box masks, transcription embeddings, and fixation maps, and a curriculum‑scheduled loss that improves accuracy and spatial alignment, yielding a 4.4% AUC gain and 13.3% F1 improvement. Stage 2 translates classifier predictions into region‑specific diagnostic sentences using confidence‑weighted keywords, an expert dictionary, and a prompted large language model, boosting clinical‑term BERTScore and ROUGE over keyword baselines.
By Tanjim Islam Riju, Shuchismita Anwar, Saman Sarker Joy, Farig Sadeque, Swakkhar Shatabda
The study evaluates the robustness of medical vision‑language models for tuberculosis screening on chest X‑rays by testing them across multiple datasets, prompts, and evaluation settings. Three specialized models (BioMedCLIP, CheXficient, MedSigLIP) and a general OpenCLIP model were audited on 12,200 images, producing 244,000 model–image–prompt scores. Results show that no model consistently outperforms others across all cohorts and reliability criteria, with prompt changes and control group composition significantly affecting AUROC, and that high training‑set performance does not reliably transfer to external cohorts.
By Mushir Akhtar, M. Tanveer, Mohd. Arshad
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
The study introduces a gaze-informed transformer framework that classifies radiologist expertise during thoracic CT interpretation by integrating eye‑tracking data into volumetric feature learning. Using a DINOv2 backbone, the model incorporates a learnable log‑space bias in self‑attention and gaze‑weighted pooling of patch embeddings. Trained on 182 CT reading sessions from five radiologists, it achieved an ROC‑AUC of 0.91 and an F1 score of 0.86, outperforming adapted baseline methods.
By Leila Khaertdinova, Anna Anikina, Claudia Mello-Thoms, Bulat Ibragimov