# Generative Models in Medical Visual Question Answering: A Survey
## 3 Dataset and Evaluation
### MedVQA Benchmark Datasets
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Data Source** & **Crusion Method** & **Size** & **Modilities** \\ \hline VQA-RAD [2] & MedPivR Radiology Database & Manual: Curated by radiologists for clinical relevance & 315 images, 3515 QA pairs & \(\times\)-ray, CT \\ \hline Slake [55] & Medical Segmentation Dexothe, NIH Chest X-ray, CHMOS & Manual: Curated by medical professionals with semantic labeling & 642 images, 14,028 QA pairs & \(\times\)-ray, CT, MRI \\ \hline PathVQA [54] & Electronic pathology textbooks, PIIR Digital Library & Manual: Curated by domain specialists for pathology-focused QA & 4998 images, 32,799 QA pairs & Pathology \\ \hline VQA-Med-2019 [59] & MedPiv Database, Radiology Images & Semi-Automatic: Patterns from medical students, validated by experts & 4200 images, 15,922 QA pairs & \(\times\)-ray, CT, MRI, Ultrasound \\ \hline PMC-VOA [14] & PubMed Central & Automated: Synthetic QA generation using NLP models and filtering & 149 K images, 227 K QA pairs & Radiology, Pathology, Microscopy \\ \hline LM-Med VQA [54] & Augmented Sake and VQA-RAD & Semi-Automatic: Atprated QA pairs with expert validation & 642 images, 14 K QA pairs & \(\times\)-ray, CT, MRI \\ \hline MMIC-COR-VOA [56] & MMC-LV, MMIC-COR & Semi-Automatic: MMIC-COR integration with expert-reviewed templates & 142,797 images, 377,981 QA pairs & \(\times\)-ray, structured EHR data \\ \hline P-VQA [57] & Disidentified Hospital Cases & Semi-Automatic & 16,366 image-related) & 16,366 image-related) \\ \hline P-VQA [58] & Clinically Generated Hospital Cases & Semi-Automatic: Generated with KCs and templates curated by physicians & 2169 images, 24,800 QA pairs & \(\times\)-ray, CT, MRI, Ultrasound \\ \hline OVQA [58] & Clinically Generated Data & Semi-Automatic: Based on clinical scenarios with expert-verified QA pairs & 2001 images, 19,020 QA pairs & \(\times\)-ray CT, MRI, Ultrasound, PET, \\ \hline MLE-VQA [27] & ROCO, Medical, MMIC-COR, PMC-VQA, & Automatic: generated with GFT4 with five levels & 5332 images, 93,969 QA pairs & \(\times\)-ray, CT, MRI, Ultrasound, PET, \\ \hline MLE-Bench [27] & PathVQA, VQA-RAD, VQA-Med, Slake & Automatic: generated with GFT4 with five levels & 1492 QA pairs & \(\times\)-ray, CT, MRI, Pathology \\ \hline \end{tabular}
\end{table} 
Table 3: MedVQA benchmark datasets. O-VQA [58] is generated using electronic medical records (EMRs) from orthopedic hospitals. It contains 2001 images from X-rays and CT modalities, along with 19,020 QA pairs created semi-automatically using templates derived from frequently asked questions (FAQs). Experienced orthopedic surgeons validated the dataset, ensuring clinical relevance and annotation quality. MLe-VQA [27] is constructed with GPT-4 with data from ROCO, Medicat, MIMIC-CXR and PMC-VQA, aiming to evaluate not only image and question comprehension but also the reasoning capability of the model. It contains 59,969 QA pairs with five levels from easy to difficult: Recognition, (e.g., identifying anatomical structures), Details (e.g., describing abnormalities), Diagnosis (e.g., identifying diseases), Knowledge Application (e.g., discussing treatment), and Reasoning, with the last two levels testing the reasoning capability. MLe-Bench [27] is a evaluation benchmark generated with selected QA pairs from PathVQA, VQA-RAD, VQA-Med [59] and Slake. It contains 1492 QA pairs that are categorized into five levels--the same as MLe-VQA.