The lack of public availability and quality annotations in medical image data has been the bottleneck for training large-scale deep learning models for many clinical downstream applications. It remains a tedious and time-consuming job for medical professionals to hand-label volumetric data repeatedly while providing a few differentiable sample cases is more logically feasible and complies with the training process of medical residents. The proposed challenge aims to advance technique in prompting large-scale pre-trained foundation models via a few data samples as a new paradigm for medical image analysis, e.g., classification tasks proposed here as use cases. It aligns with the recent trend and success of building foundation models (e.g., Vision Transformers, GPT-X, and CLIP) for a variety of downstream applications. Three private datasets for different classification tasks, i.e., thoracic disease classification, pathological tumor tissue classification, and colonoscopy lesion classification, are composed as the training (few samples) and validation sets (the rest of each dataset). Participants are encouraged to advance cross-domain knowledge transfer techniques in such a setting and achieve higher performance scores in all three tasks. The final evaluation will be conducted in the same tasks on the reserved private datasets.