MD.ai Annotator is a comprehensive platform designed to facilitate the creation of high-quality labeled datasets and the development of AI-driven clinical workflows. It enables medical professionals and researchers to efficiently annotate medical images, deploy and validate AI models, and integrate these models into clinical practice.
Key Features and Functionality:
- Native DICOM Support: Built to support the DICOM standard, the platform accommodates most DICOM imaging modalities. Users can create datasets through direct uploads, cloud storage connections, or via the DICOM C-STORE protocol. Additionally, it supports non-DICOM images (JPEG, PNG, TIFF) and videos (MP4, AVI, MOV) in custom patient-centric file structures.
- FDA 510(k)-Cleared Viewer: The web-based DICOM viewer is FDA 510(k)-cleared, enabling clinical image interpretation, review, annotation, and reporting. It supports various modalities, standard zoom/pan/windowing, hanging protocols, multiplanar reconstruction, and measurement tools, fully integrated with annotation tools.
- Scalability: The autoscaling cloud infrastructure allows seamless scaling to millions of exams, terabytes of data, and thousands of concurrent users. The user management system provides fine-grained data access control and distributed labeling task assignments.
- AI-Assisted Annotation and Model Deployment: Users can deploy models and run distributed inference on their data, utilizing models for pre-annotation or AI-assisted annotation. The platform supports federated validation across multiple sites without data sharing.
- Built-in AI Tools: The platform offers AI-powered mask segmentation tools for efficient annotation, as well as built-in PHI detection and de-identification tools to prevent sensitive data leakage.
- Developer APIs: Flexible APIs, including a CLI tool and Python client library, enable programmatic project management and control.
Primary Value and User Solutions:
MD.ai Annotator addresses the critical need for efficient and accurate annotation of medical imaging data, a foundational step in developing reliable AI models for clinical applications. By providing a scalable, secure, and user-friendly platform, it empowers medical professionals and researchers to build high-quality datasets, deploy and validate AI models, and integrate these models into clinical workflows. This accelerates the development and adoption of AI in medicine, ultimately enhancing patient care and outcomes.
