<ul data-eligibleForWebStory="true"><li>Multimodal in-context learning (ICL) in the medical domain is explored in a new study, highlighting its potential for tasks requiring adaptation from limited examples.</li><li>SMMILE, a benchmark for medical tasks, was introduced by medical experts, consisting of 111 problems covering 6 specialties and 13 imaging modalities.</li><li>The study evaluated 15 multimodal large language models (MLLMs) on SMMILE, showing moderate to poor performance in multimodal ICL abilities.</li><li>ICL contributes only a slight improvement over zero-shot performance on SMMILE, with findings indicating susceptibility to irrelevant in-context examples and the impact of example ordering.</li></ul>

SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning

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