Brain tumor MRI diagnosis demands that radiologists synthesize hundreds of images across multiple 3D sequences and longitudinal studies, imposing substantial cognitive load and increasing the risk of human error during assessment of tumor progression or treatment response. Current VLM models lack clinically verified training data and are inefficient with the analysis of 3D imagery data. Our capstone evaluates vision-language models as assistive tools for dialogue generation, using an interactive interface that mirrors clinical reasoning. By benchmarking different state-of-the-art VLMs on real-world data, we aim to reduce workload and improve consistency while preserving radiologist judgment.