# Generative Models in Medical Visual Question Answering: A Survey
## 5 Challenges and Future Directions in Generative MedVQA
### Reasoning Ability and Interpretability  
Reasoning and interpretability are fundamental to the advancement of clinical AI systems, particularly in generative MedVQA. Accurate reasoning ensures that AI models can address complex medical queries in a way that aligns with clinical logic and domain-specific requirements. Meanwhile, interpretability fosters trust and transparency, allowing clinicians to understand the rationale behind a model's responses. Together, these elements are critical for the safe and reliable integration of MedVQA systems into real-world medical workflows, where decision-making carries significant consequences.  
Recent advancements in MedVQA have introduced novel reasoning techniques tailored to handle medical complexity and enhance interpretability. MC-CoT [35] employs chain-of-thought (CoT) reasoning, where the model incrementally constructs a response  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Clinical Relevance** & **Key Limitations** \\ \hline BLEU & Measures n-gram overlap & Fails to penalize clinically invalid paraphrases \\  & Useful for detecting terminology consistency & (e.g., “GGO” vs. “ground-glass opacity”) \\ \hline ROUGE & Evaluates content recall & Ignores logical order errors \\  & Identifies missing critical information & (e.g., reversing cause–effect chains in diagnosis) \\ \hline BERTScore & Captures semantic similarity better than & Struggles with domain-specific abbreviations \\  & BLEU/ROUGE & and rare diseases \\ \hline \hline \end{tabular}
\end{table}
Table 7: Limitations of traditional NLP metrics in MedVQA task.  
by integrating intermediate outputs from modular components. By breaking down a query into logically connected sub-problems, MC-CoT ensures that the reasoning process remains transparent and clinically sound, improving the model's reliability in medical decision-making.  
Another significant advancement is rationale generation, which enhances interpretability by providing explanatory justifications alongside model outputs. The Med-Flamingo framework [25], for instance, can be prompted to generate both answers and reasoning rationales, allowing clinicians to verify the AI's thought process against textual or visual evidence. This dual-output approach strengthens user trust, facilitates error identification, and supports iterative model refinement, making AI systems more accountable and debuggable.  
Beyond rationale generation, the Uni-Med framework [36] introduces a user-driven approach to interpretability by explicitly mapping user instructions to specific answer components. This targeted mapping ensures that responses align with the clinician's intent, providing a more personalized and comprehensible reasoning process. By integrating user instructions into model inference, Uni-Med enhances AI customization and adaptability, making it a valuable tool for diverse clinical assessments.  
Moving forward, the development of reasoning and interpretability in MedVQA is expected to take a more hybrid and clinically informed approach. Standardizing reasoning protocols to align intermediate inferences with established guidelines can strengthen consistency and bolster clinical trust. Integrating expert knowledge can help models distinguish between subtle diagnostic categories and reduce misinterpretations. Designing interfaces that let clinicians probe or challenge a model's reasoning fosters transparency, especially when paired with confidence estimation to highlight areas of uncertainty. Building iterative feedback loops into the model-training process allows users to correct errors and guide updates, improving relevance to ever-evolving medical practices. As MedVQA systems take on more significant roles in clinical decision support, advancements in reasoning and interpretability will be crucial in ensuring safety, reliability, and real-world usability.