Simplifying Multimodality: Unimodal Approach to Multimodal Challenges in Radiology with General-Domain Large Language Model

arXiv:2405.01591v1 Announce Type: new
Abstract: Recent advancements in Large Multimodal Models (LMMs) have attracted interest in their generalization capability with only a few samples in the prompt. This progress is particularly relevant to the medical domain, where the quality and sensitivity of data pose unique challenges for model training and application. However, the dependency on high-quality data for effective in-context learning raises questions about the feasibility of these models when encountering with the inevitable variations and errors inherent in real-world …

abstract arxiv capability challenges cs.ai cs.cl data domain eess.iv general language language model large language large language model large multimodal models lmms medical multimodal multimodality multimodal models progress prompt quality radiology samples sensitivity simplifying the prompt type unique