# Generative Models in Medical Visual Question Answering: A Survey
## 2 The Evolution of Generative MedVQA  
### The Paradigm Shift: From Discriminative to Generative Models  
The evolution of MedVQA has seen a fundamental shift from discriminative-based approaches to generative models. Early MedVQA systems relied on classification architectures that selected answers from predefined sets, limiting their ability to address complex, open-ended medical queries that require detailed, context-sensitive responses [3, 12]. While these models demonstrate success in structured tasks such as binary and multiple-choice question answering, they struggle with flexibility and lacked the capability to generate nuanced, patient-specific explanations [13, 14]. As clinical demands grew, it becomes evident that a more expressive and adaptable approach is necessary to enhance AI-driven medical reasoning [15]. This shift has been driven by advancements in generative architectures, allowing models to move beyond rigid classification frameworks toward decoder-based approaches capable of producing free-form, context-sensitive answers. Early generative models, such as MedFuseNet [16] and CGMVQA [6], combined classification and generative techniques, using LSTM-based decoders with beam search, offering a transition between fixed answer selection and open-ended text generation. The introduction of transformer-based decoders, including BART [20] and T5 [21], further refined generative MedVQA by improving long-range dependencies and sequence-to-sequence learning [22, 23]. More recently, the integration of LLMs and MLLMs has significantly expanded the capabilities of MedVQA, becoming a popular direction as shown in Table 1. These models  
Figure 1: A representative illustration of generative MedVQA systems. Similar to classificative MedVQA systems, generative MedVQA systems typically consists of an image encoder which encodes image input, a text encoder which encodes the question input, a modality fusion module which aligns different modalities, and an answer generator that produces the answer. leverage large-scale pretraining [24; 25; 26], instruction tuning [14; 22; 24; 25], and multimodal alignment [27; 28] techniques to produce more contextually relevant and clinically useful responses. The development of parameter-efficient fine-tuning (PEFT) methods, such as prefix tuning, LoRA, and visual instruction tuning, has further enabled LLMs to be adapted for medical reasoning with reduced computational overhead [12; 13; 29]. The diversity of generative MedVQA model architectures, fusion techniques, and training strategies employed in generative MedVQA is systematically summarized in Table 2, providing a comparative overview of key works in the field following the chronological order. Over the years, MedVQA has evolved from early encoder-decoder approaches that employed CNN-based vision encoders (e.g., VGG, ResNet) and LSTM text decoders to increasingly sophisticated transformer-based architectures. Early works typically fused image and text features through simple concatenation or shallow attention mechanisms, as seen in TUA1 and CGMVQA. More recent models, including MedVInT, Uni-Med, and LLaVA-Med, integrate pretrained vision encoders (e.g., CLIP-ViT, EVA) with large language models (e.g., GPT-2, LLaMA) via cross-attention or projection modules, reflecting the broader AI trend toward multimodal alignment and instruction tuning. The emergence of frameworks like Med-Flamingo, McVLM, and MEDIFICS further underscores the shift toward MLLMs, emphasizing task-specific adapters and parameter-efficient fine-tuning methods (e.g., LoRA, QLoRA) to handle domain complexity without incurring prohibitive training costs. This transformation represents a significant leap in AI-driven medical question answering, enabling models to generate richer, more interpretable, and adaptable answers that better align with real-world clinical needs. Figure 2 provides a timeline of generative MedVQA works alongside selected discriminative models, offering an overview of the field's evolution.