  Vision-language models, while effective in general domains and showing strong
performance in diverse multi-modal applications like visual question-answering
(VQA), struggle to maintain the same level of effectiveness in more specialized
domains, e.g., medical. We propose a medical vision-language model that
integrates large vision and language models adapted for the medical domain.
This model goes through three stages of parameter-efficient training using
three separate biomedical and radiology multi-modal visual and text datasets.
The proposed model achieves state-of-the-art performance on the SLAKE 1.0
medical VQA (MedVQA) dataset with an overall accuracy of 87.5% and demonstrates
strong performance on another MedVQA dataset, VQA-RAD, achieving an overall
accuracy of 73.2%.
