If you’re trying to create training data for a medical AI model, you might have used free and open-source tools like ITK SNAP to label medical images, but they lack features for annotating efficiently and effectively. When looking at paid image labeling tools, there is an element of risk as not all tools are created equal, especially in the computer vision and healthcare space. To help find the right platform, a guide has been created to highlight seven key features to look for when choosing tools for annotating DICOM images, including native DICOM support, 3D annotation, easy-to-use interface, automated annotation of DICOM images, quality control features, audit trails, and compliance with SOC2 and HIPAA frameworks. These features can make medical image labeling more efficient while resulting in better labeled data and reduced risk.