3 papers
cs.CV2026
Vision-language models for chest radiography do not always need the image
Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams +3
Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image. That inference is unsafe: a model exploit…
eess.IV2026
From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties
Felix Busch, Tianyu Han, Marcus Makowski +3
The study evaluates and compares GPT-4 and GPT-4Vision for radiological tasks, suggesting GPT-4Vision may recognize radiological features from images, thereby enhancing its diagnos…
cs.CL2025
RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno Bressem +7
Large language models (LLMs) often generate outdated or inaccurate information based on static training datasets. Retrieval-augmented generation (RAG) mitigates this by integrating…