Given a medical image and a question in natural language, medical VQA systems are required to predict clinically relevant answers. Integrating information from visual and textual modalities requires complex fusion techniques due to the semantic gap between images and text, as well as the diversity of medical question types. To address this challenge, we propose aligning image and text features in VQA models by using text from medical reports to provide additional context during training. Specifically, we introduce a transformer-based alignment module that learns to align the image with the textual context, thereby incorporating supplementary medical features that can enhance the VQA model’s predictive capabilities. During the inference stage, VQA operates robustly without requiring any medical report. Our experiments on the Rad-Restruct dataset demonstrate a significant impact of the proposed strategy and show promising improvements, positioning our approach as competitive with state-of-the-art methods in this task.