Mined Prompting and Metadata-Guided Generation for Wound Care Visual Question Answering
This work addresses the problem of reducing provider workload in asynchronous remote care by developing AI tools for wound care, though it is incremental as it builds on existing methods for the MEDIQA-WV 2025 shared task.
The paper tackled the challenge of generating free-text responses to wound care queries with images for AI-assisted clinical support, showing that mined prompting improved response relevance and metadata-guided generation enhanced clinical precision.
The rapid expansion of asynchronous remote care has intensified provider workload, creating demand for AI systems that can assist clinicians in managing patient queries more efficiently. The MEDIQA-WV 2025 shared task addresses this challenge by focusing on generating free-text responses to wound care queries paired with images. In this work, we present two complementary approaches developed for the English track. The first leverages a mined prompting strategy, where training data is embedded and the top-k most similar examples are retrieved to serve as few-shot demonstrations during generation. The second approach builds on a metadata ablation study, which identified four metadata attributes that consistently enhance response quality. We train classifiers to predict these attributes for test cases and incorporate them into the generation pipeline, dynamically adjusting outputs based on prediction confidence. Experimental results demonstrate that mined prompting improves response relevance, while metadata-guided generation further refines clinical precision. Together, these methods highlight promising directions for developing AI-driven tools that can provide reliable and efficient wound care support.