# Generative Models in Medical Visual Question Answering: A Survey
## 4 Advances in Generative MedVQA  
### Adopting LLMs and MLLMs  
The adoption of LLMs and MLLMs has marked a significant evolution in the capabilities of MedVQA systems. These models, with their ability to integrate vast amounts of textual and visual information, have enabled MedVQA models to handle complex, open-ended queries with greater accuracy and contextual awareness and profoundly enhanced generative reasoning and conversational capabilities. This section highlights key techniques in the application of LLMs and MLLMs to MedVQA, focusing on instruction tuning, domain adaptation, and parameter-efficient fine-tuning. These methodologies exemplify how LLMs and MLLMs are tailored to meet the specialized needs of the medical field.  
**Instruction Tuning.** Adapting large (multimodal) language models (LLMs/MLLMs) to MedVQA via instruction tuning has proven effective for aligning these models with clinical requirements. Rather than a purely general-purpose setup, instruction-tuned frameworks leverage carefully designed prompts and task-specific directives--often sourced from domain experts or synthetic medical datasets--to guide model outputs. For instance, LLaVA-Med [24], MedVInT [14], and Uni-Med [36] each illustrate how refining LLMs with medical task instructions improves answer relevance, reduces hallucination, and bolsters clinical usability. The key advantage lies in steering the model's generative behavior with precise, context-sensitive cues, ultimately enhancing both accuracy and interpretability.  
**Domain Adaptation.** Beyond instruction tuning, domain adaptation techniques tailor LLM-based vision encoders and textual components to medical data. By combining pretrained components (e.g., CLIP-ViT) with fine-tuning strategies that incorporate radiology images and clinical text, these methods hone representational power on specialized domains. Such approaches, demonstrated by [PERSON]. [12], emphasize lightweight modifications (e.g., LoRA-based layers) that localize model parameters to the intricacies of radiological imagery. This targeted adjustment balances strong performance on MedVQA tasks (e.g., Slake, VQA-RAD) with reduced overhead, ensuring feasibility in clinical environments where computational resources may be constrained.  
**Parameter-Efficient Fine-Tuning (PEFT).** As LLMs and MLLMs grow in complexity, cost-effective strategies are paramount for broader adoption. PEFT methods, such as the LoRA-based fine-tuning used in PeFoMed [29], tackle the resource bottleneck by updating only a small fraction of parameters while keeping the model core intact. This selective approach not only lowers training and storage demands but also increases adaptability: new medical data or tasks can be incorporated without overhauling the entire model. In practice, PEFT techniques pave the way for MedVQA systems deployable on edge devices and in low-resource settings--a critical factor for scaling advanced AI support in global healthcare.  
Adopting LLMs/MLLMs for MedVQA increasingly hinges on three complementary strategies--instruction tuning to align outputs with clinical demands, domain adaptation to capture medical subtleties, and parameter-efficient fine-tuning to maintain feasibility across diverse healthcare contexts. Collectively, these techniques enable models to bridge the gap between general-purpose language understanding and the specialized rigor of real-world clinical applications, driving innovation and expanding access to high-quality AI solutions in medicine.