# Generative Models in Medical Visual Question Answering: A Survey
## 4 Advances in Generative MedVQA
### Automated Dataset Building and Data Augmentation
Uni-Med framework [36] goes a step further by generating datasets with built-in explanations. These explanations provide additional layers of interpretability, allowing the model to learn not only to generate answers but also to justify them. For example, when tasked with identifying abnormalities in a chest X-ray, the generated dataset includes explanations detailing why specific regions are flagged, fostering more transparent and interpretable MedVQA outputs. PMC-VQA [14] is built using PMC-OA, a biomedical corpus containing 1.6 million image-text pairs extracted from PubMed Central's Open Access subset. The dataset construction process involves automated question-answer generation, leveraging ChatGPT to synthesize five diverse question-answer pairs per image caption, covering different medical domains such as radiology, pathology, and microscopy. To ensure high-quality data, automated filtering methods were applied, including a text-only QA filtering model trained on LLaMA-7B to remove questions that could be answered without visual input. Additional manual verification was conducted to curate a high-quality test set (PMC-VQA-test-clean), ensuring relevance and diversity. The final dataset contains 227k VQA pairs across 149k images, significantly surpassing existing MedVQA datasets in size and modality diversity. The MLe-VQA dataset [27] is a multi-level instruction dataset designed to enhance progressive reasoning capabilities in MedVQA. It consists of 5352 medical images collected from public biomedical datasets like MIMIC-CSR, ROCO, MedICaT, and PMC-VQA, covering X-ray, CT, MRI, ultrasound, angiograms, pathology, and ophthalmic imaging. Images were carefully selected based on caption richness to ensure meaningful question-answer generation. The dataset includes 59,969 multi-level QA pairs, generated using GPT-4, following a structured approach to simulate the diagnostic reasoning process of a doctor. The questions are categorized into five progressive levels: Recognition, Details, Diagnosis, Knowledge Application, and Reasoning Process, which integrates a step-by-step logical explanation. GPT-4 was guided with structured prompts to ensure medical relevance, and expert manual validation was applied to enhance the dataset's accuracy. This dataset enables comprehensive training and benchmarking for MLLMs in MedVQA, helping them understand medical images, reason step-by-step, and provide clinically relevant responses. The dataset generation process in MEDIFIC involves synthetic data augmentation using GPT-3.5-Turbo to create realistic medical dialogues [38]. This process ensures the model is trained on diverse and clinically relevant question-answer pairs, improving its performance in Medical Visual Question Answering (MedVQA). The dataset is built by extracting metadata from medical image datasets such as ISIC (skin cancer), ROCO (radiology), and MURA (bone X-rays). Using few-shot prompting, GPT-3.5-Turbo generates structured doctor-patient conversations, simulating real-world clinical interactions. This enhances the model's ability to handle complex medical queries by providing context-rich training samples. The dataset also includes annotated image-text pairs, aligning radiology reports with corresponding images, ensuring multimodal learning. By leveraging synthetic data, MEDIFICS addresses the lack of large-scale MedVQA datasets, improving generalization while maintaining domain-specific accuracy. PubMedVision is a large-scale medical multimodal dataset with 1.3 million medical VQA samples [26]. The dataset is built by extracting medical image-text pairs from PubMed, refining them using GPT-4V, and generating structured question-answer pairs to enhance medical multimodal learning. Unlike previous datasets that rely solely on text-based LLM reformulation, PubMedVision employs \"unblinded\" MLLMs, allowing GPT-4V to process both images and their contextual text to generate highly aligned, clinically relevant descriptions.