arXiv:2607. 01973v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly applied in medical tasks such as pathology description, report generation, and visual question answering.
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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.
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.
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: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.
arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.
This study introduces a two‑stage vision‑language model framework to assess the clinical quality and usability of late gadolinium enhancement (LGE) cardiac MRI images used for atrial fibrillation ablation planning. The first stage employs a fine‑tuned VLM to generate structured radiology‑style reports on five quality criteria—Noise, Motion Artifact, LA Boundary Accuracy, PV Region Accuracy, and Under‑segmentation Severity—while the second stage uses a GPT‑based reasoning module to convert these reports into structured quality scores and a binary decision on clinical usability. Evaluated on a curated dataset of 60 image‑slice and text‑pair annotations from 20 patients, the InternVL2 model achieved the highest criterion‑level accuracy, and DeepSeek reached perfect agreement on the clinical usability decision.