Over the past four years, the FDA has approved over 300 AI algorithms, primarily related to medical imaging, reflecting the growing reliance on AI for accurate and faster diagnoses. The success of these AI models, particularly in medical imaging, hinges on high-quality data annotation, which is crucial for outcomes in patient care. The article provides an overview of popular tools for annotating DICOM and NIfTI files, emphasizing their features, use cases, and suitability for different teams. It highlights platforms like Encord, 3D Slicer, Labelbox, Kili, ITK-Snap, and MONAI, each offering unique capabilities for medical image annotation. These tools are designed to cater to various stakeholders, including data science teams from startups, large healthcare organizations, and computer vision teams in medical settings, providing options for free and commercial use. The text underscores the importance of these tools in enhancing data quality and speed, ultimately enabling healthcare AI models to be more effective and production-ready.