将临床风险与影像对齐，用大模型融合多模态信息提升医学诊断准确率

Medical diagnosis requires the effective synthesis of visual manifestations and clinical metadata. However, existing methods often treat metadata as isolated tags, failing to exploit the rich semantic knowledge embedded in clinical descriptions. We propose PRIMA (Pre-training with Risk-integrated Image-Metadata Alignment), a framework that integrates domain-specific knowledge into multi-modal representation learning. We first curate a corpus of risk--disease correlations via Retrieval-Augmented Generation (RAG) to refine Clinical ModernBERT, embedding diagnostic priors into the text encoder. To bridge the modality gap, we introduce a dual-encoder pre-training strategy utilizing DINOv3 and our refined Clinical ModernBERT, optimized by a suite of four complementary loss functions. These losses are designed to capture multi-granular semantic alignment and handle the ambiguity of clinical correlations through soft labels. Finally, we leverage Qwen3 to fuse these aligned features for precise disease classification. Extensive experiments demonstrate that PRIMA effectively harmonizes pixel-level features with abstract clinical expertise. Across PAD-UFES-20 and AQUA, PRIMA achieves average F1-scores of 72.04\% and 85.22\%, outperforming strong image-only, metadata-fusion, and medical vision-language baselines. Notably, our framework achieves strong performance without requiring massive data collection or exhaustive computational resources. Our code is available at https://github.com/yqwang01/PRIMA.