  Medical Visual Question Answering~(VQA) is a combination of medical
artificial intelligence and popular VQA challenges. Given a medical image and a
clinically relevant question in natural language, the medical VQA system is
expected to predict a plausible and convincing answer. Although the
general-domain VQA has been extensively studied, the medical VQA still needs
specific investigation and exploration due to its task features. In the first
part of this survey, we collect and discuss the publicly available medical VQA
datasets up-to-date about the data source, data quantity, and task feature. In
the second part, we review the approaches used in medical VQA tasks. We
summarize and discuss their techniques, innovations, and potential
improvements. In the last part, we analyze some medical-specific challenges for
the field and discuss future research directions. Our goal is to provide
comprehensive and helpful information for researchers interested in the medical
visual question answering field and encourage them to conduct further research
in this field.
