Medical visual question answering (MedVQA) requires models to provide accurate answers given a medical image and a corresponding question. Recently, instruction tuning of general large vision–language models (LVLMs) has become a dominant paradigm for this task, enabling open-ended predictions and effective integration of multimodal information. However, existing methods synthesize instruction data from image–caption pairs that primarily focus on visual attributes, rather than knowledge-level QA generation. This situation limits the model’s ability to learn relevant medical knowledge during training, thereby restricting its performance on MedVQA. Hence, this paper proposes MedKInstruct, which incorporates a multimodal medical knowledge graph (MMKG) to assist LVLMs in synthesizing knowledge-intensive instruction data. Additionally, we design an MMKG path–based reward function to train a stronger MedVQA model through reinforcement learning. Experimental results on the public datasets Slake and VQA-RAD show that MedKInstruct outperforms previous methods by 4.16% and 4.50%. The source code is available at the following link: https://github.com/Sonder-hang/MedKinstruct