Objective:

To systematically analyze recent developments in Medical Visual Question Answering (Med-VQA).

Approach:

Key Findings:

• Shift toward generative models supported by retrieval mechanisms and structured reasoning strategies.
• Generative models enable free-form clinical question answering and are more consistent than traditional classification-based methods.
• Frameworks like multi-agent and hierarchical Chain-of-Thought improve interpretability and reduce hallucinations.

Interpretation:

Limitations:

• Higher computational time required for advanced frameworks.
• Challenges in multi-view analysis and multi-lingual question answering.
• Lack of standardized evaluation and exploration in real-world clinical settings.

Conclusion:

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.