Imagine asking an AI about your medical images. Sounds futuristic, right? Visual Question Answering (VQA) models are making this a reality, allowing doctors and even patients to get quick insights from complex scans. But a crucial question looms: how reliable are these AI assistants when faced with the variety of medical images and questions they'd encounter in real-world scenarios? A new research framework called SURE-VQA tackles this head-on. Researchers found that current methods of evaluating these AI assistants aren't thorough enough. Traditional metrics often fall short, failing to grasp the subtle nuances of medical language. Furthermore, existing benchmarks don’t always reflect the diverse data found in real clinics. SURE-VQA tackles these limitations by examining how well AI models perform on real-world variations in medical images, using more sophisticated, language-aware evaluation methods. The study also used simple sanity checks to see if the model relies on the image or just uses language shortcuts. The findings? While some fine-tuning methods, like LoRA, show promise, no single approach guarantees consistent accuracy across all scenarios. Surprisingly, models sometimes answered correctly without even looking at the images, revealing biases in existing datasets. The type of data variations, like changes in imaging equipment or patient demographics, proved more important than the specific AI training method. What does this mean for the future of medical VQA? We need smarter datasets, ones with a richer mix of questions and images that truly challenge these AI assistants. We also need advanced models that can handle the subtle complexities of medical language. The quest for truly robust medical AI assistants is just beginning, but frameworks like SURE-VQA are lighting the way.