\section{Conclusion}

In conclusion, the evaluation of the proposed Difference Medical VQA model underscores its potential to assist in clinical decision-making through automated and accurate image analysis. The model demonstrated strong alignment with the ground truth in detecting prominent medical findings, suggesting its reliability in handling straightforward diagnostic tasks. Its structured and contextually relevant responses further highlight its utility in providing actionable insights.

However, the model's sensitivity to nuanced or complex medical differences, such as distinguishing subtle anatomical changes, remains an area for improvement. Addressing these limitations will require enhancing the training process with more diverse and finely annotated datasets, along with implementing advanced mechanisms for error analysis and fine-grained feature detection.

Despite these challenges, the model's superior performance metrics compared to state-of-the-art approaches, particularly in generating clinically relevant and linguistically accurate responses, set a new benchmark for Difference Medical VQA systems. These findings suggest that with continued refinement, this approach holds promise for advancing diagnostic precision and supporting radiological workflows.
