Case Study: MD.ai achieves faster, scalable medical-image annotation and dataset processing with Google Cloud Platform

MD.ai Supporting smarter medical AI research and development

MD.ai helps hospitals and research teams turn medical reports and imaging into trainable AI projects, but faced a common barrier: dataset development and high-quality annotation. Creating reliable labels often requires specially trained radiologists, public datasets are frequently mislabeled or disorganized, and legacy tools are slow or lack usable GUIs—making initial AI integration time-consuming and error-prone.

To address this, MD.ai built Annotator on Google Cloud Platform (GKE and Cloud Healthcare API) as a cloud-native, Chrome-optimized tool that natively supports DICOM/HL7, exports JSON and DICOM SR, and links to Jupyter Colab for model development. The solution cut image-management time from hours to seconds, enabled 1,300+ teams to process more than 30,000 images (including the RSNA Pneumonia Detection Challenge), and improved annotation quality and workflow speed—creating a direct path to scalable AI integrations and better patient-care insights.

MD.ai

Anouk Stein

Board Certified Radiologist