About md.ai
MD.ai is a medical imaging AI platform designed to accelerate the development and deployment of AI models in radiology. It aims to increase the efficiency and productivity of radiologists by providing AI-powered reporting and annotation tools. The platform offers solutions for data annotation, reporting workflows, and model validation, with features like native DICOM support, AI-assisted annotation, and seamless integration with EHR/HIS/RIS systems.
How to Use
MD.ai offers tools for both reporting and annotation. For reporting, users can leverage AI for template selection, dictation mapping, impression generation, and automated billing code generation. For annotation, users can build high-quality labeled datasets using the platform's native DICOM support and AI-assisted annotation features. Users can request a demo or try the reporting feature directly.
Key Features
- AI-powered reporting
- DICOM-native data annotation
- AI-assisted annotation
- Seamless EHR/HIS/RIS integration
Use Cases
- Accelerating the development and deployment of medical imaging AI models
- Supercharging clinical reporting workflows with LLMs
- Building high-quality labeled datasets for AI model training
- Streamlining administrative tasks with automated billing code generation
Key Features
Pros & Cons
- Native DICOM support ensures seamless handling of medical imaging data
- FDA 510(k)-cleared viewer adds regulatory credibility for clinical use
- LLM-powered reporting automates multiple tedious tasks (template selection, impression generation, billing codes)
- Multi-device sync allows radiologists to work from any device with consistency
- AI-assisted annotation significantly speeds up dataset creation for model training
- Simple integration with existing EHR/HIS/RIS systems via HL7/DICOM
- Pricing is not publicly available and requires contacting sales
- Integration complexity may vary depending on existing hospital IT infrastructure
- No free tier offered for testing or evaluation
- Limited publicly available information on model validation and deployment features beyond annotation