  Medical visual question answering (Med-VQA) aims to automate the prediction
of correct answers for medical images and questions, thereby assisting
physicians in reducing repetitive tasks and alleviating their workload.
Existing approaches primarily focus on pre-training models using additional and
comprehensive datasets, followed by fine-tuning to enhance performance in
downstream tasks. However, there is also significant value in exploring
existing models to extract clinically relevant information. In this paper, we
propose the Latent Prompt Assist model (LaPA) for medical visual question
answering. Firstly, we design a latent prompt generation module to generate the
latent prompt with the constraint of the target answer. Subsequently, we
propose a multi-modal fusion block with latent prompt fusion module that
utilizes the latent prompt to extract clinical-relevant information from
uni-modal and multi-modal features. Additionally, we introduce a prior
knowledge fusion module to integrate the relationship between diseases and
organs with the clinical-relevant information. Finally, we combine the final
integrated information with image-language cross-modal information to predict
the final answers. Experimental results on three publicly available Med-VQA
datasets demonstrate that LaPA outperforms the state-of-the-art model ARL,
achieving improvements of 1.83%, 0.63%, and 1.80% on VQA-RAD, SLAKE, and
VQA-2019, respectively. The code is publicly available at
https://github.com/GaryGuTC/LaPA_model.
