High-dimensional medical data presents unique challenges for multimodal systems, exhibiting intricate spatial and temporal patterns that resist conventional modeling approaches. Traditional approaches typically process these modalities that create significant barriers to clinical deployment and cross-modal knowledge transfer. Motivated by recent successes in in-context learning (ICL) for biomedical applications, we investigate whether large language models (LLMs) can perform medical prediction tasks through structured graph-based prompts in a fully training-free manner. We propose a unified framework that introduces a novel dual-level representation approach: mathematical dimensionality reduction techniques such as PCA construct text-level inputs, while a training-free Graph Transformer generates embedding-level representations from multi-modal graph structural information. Through experiment, we establish that the combination of Universal Graph Transformer with additional text-level representations achieves optimal performance with comparable performance to the pretrained method, our training-free paradigm eliminates the need for complex cross-modal alignment modules and extensive parameter optimization, demonstrating the feasibility of leveraging LLMs for medical graph analysis without task-specific training.