The paper introduces PubMed-Ophtha, a hierarchical dataset of 102,023 panels with subcaptions from 15,842 open‑access PubMed Central articles, designed to provide high domain density for vision‑language models in ophthalmology. The authors fine‑tuned identical CLIP models on this dataset, on medical reports, and on general biomedical literature, and found that the domain‑specific literature model achieved the best average performance across 110 clinical tasks, with a mean linear probing AUROC of 88.63% versus 85.68% for medical reports. Even when restricting the dataset to fundus images or matching the image count of the medical report dataset, performance remained high, suggesting that the gains stem from the domain density of the literature.
By Verena Jasmin Hallitschke, Carsten Eickhoff, Philipp Berens
An end-to-end-trained vision-language model generates prostate biopsy reports in native languages, demonstrated in German. The system uses a tokenizer and model trained from scratch and an automated pipeline that splits composite reports into image-text pairs, producing 17,344 pairs from 2,402 cases without manual annotation. Evaluated on clinical attributes, it achieves 96.2% F1 for malignancy detection and 65.2% for Gleason grading, comparable to an FDA-cleared classifier and validated on external cohorts.
By Christian Grashei, Fabian G\"ulhan, Maximilian Legnar, Fabian St\"ogbauer, Cleo-Aron Weis, Carolin Mogler, Peter Sch\"uffler
arXiv:2606. 25375v2 Announce Type: replace-cross Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud.
By Ching-Hao Chiu, Hao-Wei Chung, Gelei Xu, Xueyang Li, Pin-Yu Chen, John Kheir, Meysam Ghaffari, Carlos Morato, Ahmed Abbasi, Yiyu Shi
As vision-language models (VLMs) are increasingly applied to medical AI, existing benchmarks mainly focus on evaluating their diagnosis ability over given medical images and texts, implicitly assuming that standardized medical images, texts or question-answer pairs are already prepared. However, this assumption does not hold when we apply VLMs in real clinical practice, where medical data is often raw, heterogeneous, and fragmented across different sources.
arXiv:2609.15180v1 Announce Type: new
Abstract: Vision-language models are increasingly explored for clinical prediction from electronic health records and medical images, where identifying unreliabl...
By Mingcheng Zhu, Jinning Liang, Tingting Zhu
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 paper introduces the Medical Data Standardization Benchmark (MDS‑Bench), which evaluates vision‑language models (VLMs) on their ability to process raw, heterogeneous medical data. Models must identify source formats, convert raw images into VLM‑compatible inputs, extract relevant text, and organize the results into structured image‑text pairs. Experiments show that even the top VLM, Gemini 3 Flash, achieves only a 48.6% end‑to‑end success rate, underscoring the challenge of raw data standardization in clinical settings.
By Xin Chen, Dongliang Xu, Cunhao Zhu, Xudong Luo, Haoyang Lyu, Xiaoxiao Sun, Serena Yeung-Levy, Yue Yao
arXiv:2607. 03650v1 Announce Type: cross Abstract: Extracting textual information from scanned medical documents, such as external laboratory reports and manually filled forms, has been a major challenge in modern electronic health records (EHRs).
By Enshuo Hsu, Jin Zhou, Kirk Roberts
arXiv:2601.06847v2 Announce Type: replace-cross
Abstract: Vision-Language Models (VLMs) can generate convincing clinical narratives, yet frequently struggle to visually ground their statements. We po...
By Mengmeng Zhang, Xiaoping Wu, Hao Luo, Fan Wang, Yisheng Lv
The paper evaluates uncertainty estimation (UE) methods for clinical vision‑language models (VLMs) on visual question answering (VQA). Across 8 UE techniques and 12 VLMs, UE quality tracks model accuracy, degrading where performance is weakest, and fails to signal uncertainty when models are stressed by hiding the correct answer (NOTA perturbations). However, UE on unperturbed inputs reliably predicts which predictions will collapse under NOTA, suggesting UE can diagnose model fragility.
By Arnisa Fazla, Alberto Testoni, Ameen Abu-Hanna, Barbara Plank, Iacer Calixto
MVC-Bench is a new benchmark designed to evaluate the calibration of vision‑language models (VLMs) and medical VLMs (Medical‑VLMs) for medical image classification. It tests calibration across robustness to modality, backbone, and domain shift; effectiveness of calibration strategies and prompt‑tuning methods; and stability under prompt‑template and random‑seed variations. The benchmark includes eight backbones, three medical modalities (fundus imaging, histopathology, chest X‑ray), and compares post‑hoc, train‑time, and zero‑shot calibration approaches, reporting accuracy, Expected Calibration Error (ECE), Maximum Calibration Error (MCE), and Adaptive Calibration Error (ACE) over 1,638 experiments, while also proposing a Multi‑Class Margin (MCM) regularization technique that improves ECE in most settings.
By Ashshak Sharifdeen, Shihab Aaqil Ahamed, Ufaq Khan, Muhammad Akhtar Munir Sujair Ibrahim, Mohamed Rafeek Mareer Ahamed, Yutong Xie, Imran Razzak, Muhammad Haris Khan
arXiv:2607. 05310v1 Announce Type: new Abstract: Model editing promises a fast, targeted way to correct post-deployment mistakes in medical vision-language models (VLMs) without costly retraining.
By Guli Zhu, Chenwei Wu, Liyue Shen