Medical image classification is a very fundamental and crucial task in the field of computer vision. These years, CNN-based and Transformer-based models are widely used in classifying various medical images. Unfortunately, The limitation of CNNs in long-range modeling capabilities prevent them from effectively extracting fine-grained features in medical images , while Transformers are hampered by their quadratic computational complexity. Recent research has shown that the state space model (SSM) represented by Mamba can efficiently model long-range interactions while maintaining linear computational complexity. Inspired by this, we propose Vision Mamba for medical image classification (MedMamba). More specifically, we introduce a novel Conv-SSM module, which combines the local feature extraction ability of convolutional layers with the ability of SSM to capture long-range dependency. To demonstrate the potential of MedMamba, we conduct extensive experiments using three publicly available medical datasets with different imaging techniques (i.e., Kvasir (endoscopic images), FETAL_PLANES_DB (ultrasound images) and Covid19-Pneumonia-Normal Chest X-Ray (X-ray images)) and two private datasets built by ourselves. Experimental results show that the proposed MedMamba performs well in detecting lesions in various medical images. To the best of our knowledge, this is the first Vision Mamba tailored for medical image classification. The purpose of this work is to establish a new baseline for medical image classification tasks and provide valuable insights for the future development of more efficient and effective SSM-based artificial intelligence algorithms and application systems in the medical. Source code has been available at https://github.com/YubiaoYue/MedMamba.