Multimodal learning is a specialized field that focuses on developing and training models capable of utilizing various types of data and their relations. The goal is to enable these models to understand the relationships between different modalities and effectively combine them to enhance prediction performance. One prominent example of a multimodal learning framework is CLIP. This framework has been trained on millions of image-text pairs and has demonstrated comparable zero-shot performance to fully supervised models . Based on this idea, MedCLIP introduces a novel strategy that replaces the InfoNCE loss with a medical knowledge-based semantic matching loss. The objective of this adjustment is to tackle the issue of false negatives in contrastive learning .