MD.ai

MD.ai is a pioneering platform designed to accelerate the development and deployment of artificial intelligence in medicine, with a specialized focus on medical imaging. Founded by Harvard, Duke, and Columbia-trained doctors, the platform addresses the critical needs of both AI developers and clinical practitioners. It offers a dual-pronged solution: a powerful data annotation tool for building high-quality AI models and an intelligent, AI-driven reporting system that revolutionizes the daily workflows of radiologists.

The platform is trusted by top academic medical institutions, large pharmaceutical companies, and healthcare organizations worldwide. It has been instrumental in major AI research initiatives and competitions, including collaborations with the Radiological Society of North America (RSNA), SIIM, and Kaggle, to create large-scale, expertly annotated public datasets for conditions like pneumonia, intracranial hemorrhage, and pulmonary embolism.

How to use MD.ai

MD.ai offers two distinct but interconnected products:

For AI Development (Annotator):

1. Data Upload & Management: Users can upload native DICOM medical imaging data directly to the secure, cloud-based platform.
2. Annotation & Labeling: Utilize a suite of AI-assisted annotation tools within an FDA 510(k)-cleared viewer to accurately label anatomical structures, pathologies, and other findings. The tools include features for PHI detection and de-identification to ensure patient privacy.
3. Collaboration: Teams of doctors, scientists, and engineers can collaborate in real-time to create and review large, high-quality labeled datasets.
4. Model Training & Validation: Export the curated datasets to train and validate machine learning models. Developers can leverage the platform's APIs for seamless integration into their MLOps pipelines.

For Clinical Practice (Reporting):

1. Integration: The reporting system integrates seamlessly with existing hospital infrastructure like EHR, HIS, and RIS via standard HL7/DICOM protocols.
2. Automated Workflow: When a radiologist opens a study, the system uses AI to automatically select the appropriate report template based on the study type and context.
3. AI-Powered Dictation & Generation: The radiologist can use their preferred input device (keyboard, voice commands, Philips SpeechMike, or a synchronized mobile device) to dictate findings. The LLM-powered system maps key findings, automatically generates the impression section, and can even compare findings with prior reports.
4. Review & Finalize: The system proofreads the report for accuracy and can automatically insert clinical guidelines (e.g., BI-RADS, LI-RADS).
5. Sign-off & Distribution: Upon signing, the system automatically generates billing codes and distributes the report in various formats (HL7, DICOM, PDF, email) to multiple destinations. It can also generate patient-friendly audio messages.

Core Features of MD.ai

• AI-Powered Reporting: Utilizes Large Language Models (LLMs) for automatic template selection, key findings mapping, impression generation, proofreading, and automated billing code generation.
• DICOM-Native Annotation: An FDA 510(k)-cleared viewer with AI-assisted tools for creating high-quality, labeled datasets for model training.
• Seamless Integration: Simple HL7/DICOM integration with existing EHR/HIS/RIS systems, making it a turnkey solution for clinical environments.
• Multilingual Support: The reporting tool supports over 12 languages, including real-time translation, transcription, and localized patient messages.
• Multi-Device Compatibility: Works across desktops, laptops, tablets, and mobile devices with synchronized functionality.
• Enhanced Security & Compliance: Fully HIPAA compliant with real-time compliance monitoring, regular third-party audits, and robust PHI de-identification features.
• Developer APIs: Provides APIs for developers to integrate the annotation platform into their custom AI development workflows.
• Contextual AI Chat: A HIPAA-compliant chat feature for report analysis, interpretation, and answering questions for both providers and patients.

Use Cases for MD.ai

For Medical Researchers and AI Developers:

• Creating large, expertly annotated datasets for training and validating diagnostic AI models.
• Hosting AI challenges and competitions to advance medical imaging research, as demonstrated with RSNA and Kaggle.
• Validating AI model performance in a collaborative, cloud-based environment.

For Radiologists and Clinical Practices:

• Dramatically increasing reporting efficiency and productivity by automating repetitive tasks.
• Improving diagnostic accuracy and consistency with AI-driven proofreading and guideline integration.
• Streamlining administrative tasks through automated billing code generation.
• Enhancing patient communication and education with simplified, patient-friendly audio reports.

Advantages of MD.ai

Clinician-Centric Design: Founded and designed by doctors, ensuring the tools are intuitive and address real-world clinical challenges.

End-to-End Platform: Uniquely combines the tools for AI model creation (Annotator) and clinical implementation (Reporting) in a single ecosystem.

Proven Credibility: Extensive involvement in high-profile research projects and partnerships with leading medical societies validates its expertise and reliability.

Robust Security: A strong commitment to data security and patient privacy with HIPAA compliance and regular audits.

Scalability: The cloud-native platform is designed to scale seamlessly from small research projects to large enterprise-level hospital deployments.

Pricing and Plans

MD.ai's pricing information is not publicly listed on their website. As an enterprise-grade solution for healthcare and research institutions, they likely offer customized pricing plans based on the specific needs, scale of deployment, and products required (Annotator, Reporting, or both). To get detailed pricing, potential customers are encouraged to contact the MD.ai sales team or request a demo through their website.