High-quality medical imaging datasets are crucial for the success of machine learning models in healthcare, as they directly affect the accuracy and reliability of AI-driven diagnoses. Creating these datasets involves overcoming challenges such as ensuring diversity and sufficient sample size to avoid bias, maintaining regulatory compliance, and using advanced annotation tools to enhance the accuracy and efficiency of labeling processes. Poor-quality datasets can lead to biased outcomes, misdiagnoses, and wasted resources, emphasizing the need for precise data handling and annotation. The complexity of medical imaging data, which often includes extensive metadata and multiple formats like DICOM and NIfTI, necessitates sophisticated tools and methodologies to streamline collaboration among clinical operations, annotation teams, and machine learning engineers. Ensuring data security, efficient storage, and transfer processes is also critical, especially given the large volumes of data involved. Solutions like Encord's annotation platform offer AI-assisted tools and automation to improve the quality of data annotations, helping medical professionals and data scientists address the challenges of computer vision in healthcare.