MedNeXt is a fully ConvNeXt architecture designed for 3D medical image segmentation, customized to handle sparsely annotated medical imaging datasets. The training framework is built on top of nnUNet (v1), providing a self-configuring deep learning pipeline adapted for volumetric medical imaging tasks. It leverages the scalability of modern ConvNeXt blocks while being tailored for challenges specific to medical image analysis.