AI RESEARCH
CURE: A Multimodal Benchmark for Clinical Understanding and Retrieval Evaluation
arXiv CS.AI
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ArXi:2603.19274v1 Announce Type: cross Multimodal large language models (MLLMs) nstrate considerable potential in clinical diagnostics, a domain that inherently requires synthesizing complex visual and textual data alongside consulting authoritative medical literature. However, existing benchmarks primarily evaluate MLLMs in end-to-end answering scenarios. This limits the ability to disentangle a model's foundational multimodal reasoning from its proficiency in evidence retrieval and application. We