@inproceedings{karim-uzuner-2025-masonnlp-mediqa,
    title = "{M}ason{NLP} at {MEDIQA}-{WV} 2025: Multimodal Retrieval-Augmented Generation with Large Language Models for Medical {VQA}",
    author = "Karim, A H M Rezaul  and
      Uzuner, Ozlem",
    editor = "Ben Abacha, Asma  and
      Bethard, Steven  and
      Bitterman, Danielle  and
      Naumann, Tristan  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 7th Clinical Natural Language Processing Workshop",
    month = oct,
    year = "2025",
    address = "Virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-felix-schneider-kit/2025.clinicalnlp-1.10/",
    pages = "84--94",
    abstract = "Medical Visual Question Answering (MedVQA) enables natural language queries over medical images to support clinical decision-making and patient care. The MEDIQA-WV 2025 shared task addressed wound-care VQA, requiring systems to generate free-text responses and structured wound attributes from images and patient queries. We present the MasonNLP system, which employs a general-domain, instruction-tuned large language model with a retrieval-augmented generation (RAG) framework that incorporates textual and visual examples from in-domain data. This approach grounds outputs in clinically relevant exemplars, improving reasoning, schema adherence, and response quality across dBLEU, ROUGE, BERTScore, and LLM-based metrics. Our best-performing system ranked 3rd among 19 teams and 51 submissions with an average score of 41.37{\%}, demonstrating that lightweight RAG with general-purpose LLMs{---}a minimal inference-time layer that adds a few relevant exemplars via simple indexing and fusion, with no extra training or complex re-ranking{---} provides a simple and effective baseline for multimodal clinical NLP tasks."
}