AI in healthcare is revolutionizing patient outcomes by aiding in tasks such as disease detection and patient outcome prediction, but its success is heavily dependent on high-quality labeled data, which necessitates robust data labeling platforms. These platforms, exemplified by Encord, streamline the annotation of complex healthcare data by integrating domain expertise, workflow management, and compliance features, ensuring accurate annotations that are critical for AI model performance. Encord stands out among competitors like Labelbox and Scale AI due to its advanced annotation tools, AI-assisted labeling, scalability, healthcare-specific workflows, and HIPAA-compliant infrastructure, making it particularly suited for medical applications. Best practices for using such platforms include establishing clear labeling guidelines, leveraging domain expertise, ensuring quality control, and utilizing automation to enhance efficiency and accuracy. Successful implementations, like that of healthcare AI teams using Encord, demonstrate significant improvements in labeling efficiency and AI development, ultimately contributing to better patient outcomes.