# Generative Models in Medical Visual Question Answering: A Survey
## 1 Introduction  
Medical Visual Question Answering (MedVQA) involves responding to questions related to medical images [1]. This emerging field lies at the intersection of artificial intelligence (AI) and healthcare, where machine learning models are developed to interpret and analyze medical imagery, such as X-rays, MRIs, CT scans, and pathology slides [2]. MedVQA aims to enhance clinical decision-making by providing accurate and contextually relevant answers to questions posed by clinicians, thereby alleviating medical professionals' burden, reducing diagnostic errors, and ultimately improving patient outcomes [3, 4, 5]. A fully developed MedVQA system can also benefit patients by integrating into online consultation platforms. It provides reliable answers in scenarios where medical professionals are unavailable, such as remote or automated health examinations. This capability helps reduce the risk of misinformation that can arise from online searches [4, 5, 6]. As asignificant advancement in multimodal learning, MedVQA addresses societal challenges by expanding access to high-quality healthcare and fostering informed decision-making. A typical MedVQA framework consists of four key components: an image feature extractor, a question feature extractor, a feature fusion module, and an answering component [4]. The ImageClef 2018 Challenge first introduced MedVQA task and received five model submissions, with most participants employing deep learning techniques, leveraging sequence-to-sequence learning, encoder-decoder frameworks, CNNs for image encoding, RNNs for question processing, and stacked attention networks (SAN), multimodal compact bilinear (MCB) pooling or multimodal factorized bilinear (MFB) pooling for modality fusion [1]. To address data scarcity, meta-learning was introduced to enhance model training with limited labeled data [7]. Conditional reasoning was later explored to improve modality fusion [8]. Efforts to refine visual and textual representations led to the integration of contrastive learning in PubMedCLIP [9] and masked language modeling (MLM) pretraining tasks in MMBERT [5]. Further advancements leveraged multi-task learning for image feature pretraining, enhancing image comprehension [10]. MedViLL introduced a novel vision-language pretraining (VLP) strategy, incorporating MLM, image-report matching (IRM), and a bi-directional auto-regressive self-attention mask to improve vision-language understanding [11]. While substantial research efforts have been directed towards advancing MedVQA, the predominant focus of current endeavors lies in discriminative approaches. These models predict the most appropriate answer from a predefined set of candidates from the training set, which limits their ability to generate free-form responses, particularly when addressing out-of-domain or open-ended questions [3; 12]. Consequently, such models struggle to harness the full potential of MedVQA, especially in complex and less structured scenarios in real-world clinics, calling for more solutions to the challenge [13; 14; 15]. On the other hand, recent advancements in LLMs, particularly transformer-based architectures, have catalyzed a significant paradigm shift in MedVQA. These models, renowned for their ability to handle complex natural language tasks, have driven the transition from traditional discriminative approaches to generative frameworks, enabling MedVQA systems to produce open-ended, contextually nuanced answers beyond predefined categories. Generative MedVQA, in comparison with the discriminative MedVQA framework, uses a generator instead of a classifier as the answer production module, as shown in Figure 1. In as early as 2020, CGMVQA first added a generator branch to deal with open-ended questions, producing answers with beam search algorithm, while keeping the classifier for closed-ended questions [6]. In the following two years, MedFuseNet [16] and MED-GPVS [17] also tried out different answer generators.