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hgkai26 at MEDIQA-EVAL 2026: Automated Evaluation of Visual Medical Question Answering Using LLM-as-a-Judge
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hgkai26 at MEDIQA-EVAL 2026: Automated Evaluation of Visual Medical Question Answering Using LLM-as-a-Judge
As there is a rise in the use of multimodal large language models (LLMs) for medical response generation, it is necessary to have reliable automated evaluation mechanisms that can assess the quality of model-generated outputs. The MediQA-Eval 2026 shared task focuses on grading AI-generated dermatology and wound care responses using structured human-aligned rubrics. In this work, we explore a zero-shot multimodal LLM-as-a-Judge framework to assess candidate responses across multiple quality dimensions. System performance is evaluated using the official task metrics designed to reflect alignment with human judgments. Our findings provide preliminary insights into the feasibility and limitations of LLM-based evaluators for rubric-guided medical response assessment.
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