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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.
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.
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.
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...
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.