# Generative Models in Medical Visual Question Answering: A Survey
## 3 Dataset and Evaluation  
### MedVQA Benchmark Datasets  
There are several key benchmark datasets in MedVQA research, namely VQA-RAD [2], PathVQA [54], and Slake [55]. Recently, new MedVQA datasets are crafted using semi-automatic or automatic methodologies with more data volume, scope, and modalities to better match the real-world clinical settings. This subsection briefly introduces the benchmark datasets including the data sources, creation methods, as well as dataset sizes and contents, presenting a summary table of MedVQA benchmark datasets in Table 3.  
VQA-RAD [2] contains 3515 QA pairs curated manually by radiologists, derived from the MedPix\(\otimes\) Radiology Database. It primarily focuses on X-rays and CT scans, ensuring clinical relevance and high-quality annotations for radiology-based queries. It is the earliest manually-curated MedVQA dataset and the most used MedVQA benchmark despite its relatively small size. Slake [55] is a semantically labeled and knowledge-enhanced medical VQA dataset with 642 radiology images and 14,028 QA pairs in both English and Chinese. It includes segmentation masks, bounding boxes, and a structural knowledge graph of 2603 English and 2629 Chinese triplets, enabling diverse and complex queries like organ functions and disease prevention. Images and questions are carefully annotated and reviewed by experienced physicians, making Slake a highly comprehensive and clinically relevant resource. PathVQA [54] focuses on pathology images, containing 32,800 QA pairs manually curated by domain specialists. With 5000 images, this dataset ensures high relevance to pathology diagnostics and supports reasoning-intensive QA tasks. It is the most recognized benchmark for pathological MedVQA. VQA-Med-2019 dataset from ImageCLEF 2019 VQA-Med competition [39] focuses on answering clinically relevant questions based on radiology images. It includes 4200 images and 15,292 QA pairs divided into four categories: modality, plane, organ system, and abnormality. The dataset was semi-automatically created using question patterns inspired by medical students, ensuring natural phrasing, and was validated by experts for the test set. This dataset supports the development of MedVQA systems with high-quality, clinically grounded QA pairs. PMC-VQA [14] leverages automatic synthetic QA generation through NLP models, resulting in 227,000 QA pairs across 149,000 images. Covering radiology, pathology, and microscopy, the dataset relies on automated filtering, leading to moderate annotation quality. IAI-Med VQA [36] integrates AI-driven QA generation with expert validation, producing 14,000 QA pairs from 642 images. Covering X-rays, CT, and MRI modalities, the dataset combines automation and manual validation for high-quality outputs. MIMIC-CXR-VQA [56] integrates structured EHR data from MIMIC-IV with chest X-ray images from MIMIC-CXR. Offering 46,152 QA pairs from 377,110 images, the dataset is created semi-automatically using expert-reviewed templates and machine annotations, making it ideal for multimodal medical reasoning. P-VQA [57] is designed to address patient-oriented Medical Visual Question Answering, incorporating real hospital cases. It features 2169 images spanning multiple modalities, including X-rays, CT scans, MRI, and ultrasound, alongside 24,800 QA pairs. The dataset was semi-automatically generated using templates curated by physicians and enriched with a medical knowledge graph, ensuring clinically relevant and high-quality annotations.