RedBrick AI

Teams developing radiology and medical-imaging AI can use RedBrick AI to create and review labeled datasets from imaging studies. Its browser-based viewer supports DICOM and other 2D, 3D, and video medical images, including CT, MRI, X-ray, and ultrasound. Annotators can create segmentations, landmarks, measurements, bounding boxes, and other labels, using tools such as interpolation and AI-assisted segmentation to work across image slices. Projects organize tasks and route them through configurable annotation and review stages. Teams can set labeling standards, check annotations with validation rules, compare work from multiple annotators, and search or analyze datasets and DICOM metadata. APIs, an SDK, and a CLI support data import, export, task assignment, and integration with machine-learning workflows; teams can also connect their own or third-party storage. It is designed for researchers and organizations building or validating medical-imaging AI models.

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