# Generative Models in Medical Visual Question Answering: A Survey
## 5 Challenges and Future Directions in Generative MedVQA  
### Dataset Limitations  
While existing datasets have advanced MedVQA research, their limitations must be critically addressed to guide future improvements. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**\#Parameters**} & \multirow{2}{*}{**Generative**} & \multirow{2}{*}{**LLM/MLLM-Based**} & \multicolumn{3}{c}{**Accuracy on Open-Ended Questions**} \\ \cline{4-6}  & & & & **Slake** & **VQA-RAD** \\ \hline Uni-Med & 7B & Yes & Yes & **85.3** & **74.2** \\ \hline Med-MoE & 3.6B & Yes & Yes & **85.1** & 58.6 \\ \hline MedVInT & 7B & Yes & Yes & 84.5 & 73.7 \\ \hline LLaVA-Med & 13B & Yes & Yes & 84.5 & 64.4 \\ \hline Ha, fusion & Not Reported & Yes & Yes & 84.5 & 57.5 \\ \hline van Sonsbeek & 1.5B & Yes & Yes & 84.3 & - \\ \hline PeFoMed & 57M & Yes & Yes & 77.8 & 62.6 \\ \hline BiomedCGPT & 182M & Yes & No & 84.3 & 60.9 \\ \hline M2I2 & 252M & Yes & No & 74.7 & 61.8 \\ \hline MUMC & 211M & Yes & No & 71.5 & - \\ \hline MPR & Not Reported & Yes & No & 62.6 & **77.5** \\ \hline BiomedCLIP & 422M & No & No & 82.5 & 67.6 \\ \hline ARL & 362M & No & No & 81.9 & 67.6 \\ \hline MAAE & Not Reported & No & No & 80.3 & 67.2 \\ \hline MMBBERT & Not Reported & No & No & 79.5 & 55.3 \\ \hline VQAMIX & Not Reported & No & No & - & 56.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of generative MedVQA and classic discriminative MedVQA models. **Data Bias. ** Both manual and automated datasets exhibit data bias through two primary mechanisms, originating from data sources and annotation processes. _(1) Case selection bias._ MedVQA encompasses a wide range of cases while diseases follow extreme long-tail patterns, which makes it challenging for individual datasets to represent the full spectrum of clinical scenarios. For instance, MIMIC-CXR-VQA [56] predominantly focuses on chest radiographs, while PathVQA [54] extracts pathology images and captions largely from textbooks, resulting in a focus on commonly documented cases with standardized professional language. _(2) Modality bias._ Medical images contain a variety of modalities, including radiology, pathology, microscopy, etc. Most of the existing datasets exhibit bias toward certain modalities, which potentially over-represent common modalities (e.g., X-ray) while under-representing rare emerging imaging techniques. PMC-VQA [61] covers various modalities and diseases in 149 K images though, 80% of them are radiological images obtained from PubMed Central. **Insufficient Annotation. ** Many of the existing MedVQA datasets face critical limitations in annotation sufficiency and diversity. While Slake [55] provides bilingual (English and Chinese) annotations, most datasets like VQA-RAD [2] and PathVQA [54] rely solely on English annotations. This restricts cross-lingual generalization and creates barriers for non-English medical contexts. Although image-caption datasets (e.g., ROCO) are generally used and adequate for model training/fine-tuning to some extent, other forms of annotations are equally crucial for achieving improved performance on MedVQA models. For instance, both Slake [55] and P-VQA [57] provide knowledge graphs to enhance models' diagnostic and analytical capabilities, offering additional critical information beyond pure imaging data. Slake further provides labeled segmentations of organs and tumors or bounding boxes on objects, enabling precise region-of-interest (ROI) localization for visual models. Additionally, MIMIC-CXR-VQA [56] connects medical images with structured electronic health records (EHRs) of corresponding patients, which contain multi-layered information for question answering. These supplementary annotations can effectively improve models' performance and interpretability through structured clinical correlation. **Risks in Automatic Generation. ** Stemming from the complexities of annotation, an increasing number of datasets are prompting LLMs to create QA pairs either directly or indirectly.