# Generative Models in Medical Visual Question Answering: A Survey
## 2 The Evolution of Generative MedVQA
### Explorations of Employing LLMs  
The integration of LLMs into MedVQA has significantly enhanced the ability of models to generate flexible, context-aware responses. Many works integrate frozen LLMs with vision encoders via a simple MLP projector, requiring efficient fine-tuning techniques to bridge the gap between modalities. Recent works employ LoRA, prefix tuning, and instruction tuning to adapt LLMs without excessive computational overhead. The other challenge is medical knowledge integration, where models incorporate domain-specific knowledge, instruction datasets, and staged training pipelines to improve contextual understanding. This subsection reviews various methods for employing LLMs in MedVQA, examining how researchers optimize model architectures and training strategies to improve performance while maintaining efficiency. [PERSON] et al. [13] employ prefix tuning to adapt LLMs to medical VQA tasks. Their model follows a two-stream encoder-decoder framework, where a vision encoder (e.g., CLIP with a ViT backbone) extracts image features that are mapped into learnable visual tokens via an MLP-based mapping network. These tokens are combined with tokenized question embeddings in the language encoder before passing into an LLM decoder (GPT-2 XL, BioGPT, or BioMedLM) for autoregressive answer generation. To overcome the limitation of small medical datasets, parameter-efficient fine-tuning (PEFT) methods such as prefix tuning, prompt tuning, and LoRA are used to efficiently adapt the model without extensive full-model fine-tuning. The pretraining process leverages medical text corpora and optimizes for MLM, ITM, and contrastive learning to enhance vision-language alignment. [PERSON] et al. [12] propose a transformer-based fusion framework inspired by BLIP-2 [50], integrating a biomedical-adapted vision encoder with an LLM specialized for radiology. The model employs either BiomedCLIP-ViT or PMC-CLIP ResNet50 as the vision encoder, while RadBloomz-7B serves as the LLM, with an MLP projector aligning their embeddings. The training process first focuses on medical concept alignment via image-caption pretraining on PMC-OA. The model then undergoes general MedVQA pretraining on PMC-VQA to enhance domain-specific reasoning. The final stage involves fine-tuning on radiology datasets like VQA-RAD and Slake to improve medical image interpretation. LoRA is applied to fine-tune the LLM while keeping the vision encoder frozen, ensuring efficient adaptation. PeFoMed [29] adapts LLMs with PEFT techniques for both MedVQA and medical report generation (MRG). The model consists of a frozen ViT-based EVA encoder, LLaMA2-7B for text generation, and a lightweight linear projection layer to align image and text representations. The training process includes image captioning pretraining using ROCO, CLEF2022, MEDICAT, and MIMIC-CXR for vision-language alignment, followed by task-specific fine-tuning on MedVQA datasets (VQA-RAD, Slake, PathVQA) and MRG datasets(IU-Xray). By fine-tuning only the projection layer and LoRA adapters, the model significantly reduces computational costs while preserving pretrained knowledge. MedVInT (Medical Visual Instruction Tuning) [14] introduces a visual instruction-tuning framework to bridge a pretrained vision encoder with an LLM. The visual encoder (PMC-CLIP-ResNet) maps extracted image features into a unified embedding space via a trainable projection module. The language encoder (PMC-LLaMA) encodes the input question with a structured prompt to guide response generation. The multimodal decoder has two variants: MedVInT-TE (encoder-based transformer), which reformulates answer generation as MLM, and MedVInT-TD (decoder-based transformer), which directly generates free-form text answers. The pretraining consists of PMC-VQA alignment, followed by fine-tuning on benchmarks like VQA-RAD and Slake. MMCAP (Multimodal Concept Alignment Pretraining) [28] integrates medical knowledge graphs (KGs) with vision-language pretraining to improve knowledge-intensive MedVQA.