MD.ai

by MD.ai  · Based in United States → — AI-powered reporting and annotation for radiology
Radiology
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Overview

MD.ai provides an AI platform designed to accelerate the development and deployment of AI models in medical imaging and enhance the efficiency of clinical reporting workflows. The platform offers two main products: MD.ai Reporting and MD.ai Annotator. MD.ai Reporting leverages Large Language Models (LLMs) to supercharge clinical reporting workflows with features like automatic template selection, key findings dictation mapping, impression generation, and automated billing code generation. It also aims to improve patient communication with patient-friendly audio messages. MD.ai Annotator is a DICOM-native data annotation tool that enables doctors and researchers to create high-quality labeled datasets, deploy and validate AI models, and build AI-driven clinical workflows. The platform supports seamless scaling, AI-assisted annotation, PHI detection and de-identification, and offers developer APIs. Founded by Harvard/Duke/Columbia-trained doctors, MD.ai’s mission is to improve patient care and outcomes by boosting the efficiency and productivity of healthcare providers.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • AI-powered clinical reporting with LLMs
  • Automatic template selection
  • Key findings dictation mapping
  • Impression generation
  • Automated billing code generation
  • Patient-friendly audio messages
  • DICOM-native data annotation tools
  • AI-assisted annotation
  • PHI detection and De-ID
  • Developer APIs
  • Multilingual support

Use Cases

  • Accelerating medical imaging AI development and deployment
  • Supercharging clinical reporting workflows for radiologists
  • Building high-quality labeled datasets for AI model training and validation
  • Streamlining administrative tasks in radiology
  • Improving patient communication and education
  • Collaborative medical AI research

What Physicians Need to Know

Healthcare API Support (FHIR/HL7)
MD.ai offers simple HL7/DICOM integration with EHR/HIS/RIS. They leverage Google Cloud and Cloud Healthcare API. Their reporting can be integrated into PACS, RIS, or web applications using iframes and secure token-based authentication. Reports can be sent to multiple destinations in various formats, including HL7, DICOM, PDF, and email.
HIPAA-Compliant Infrastructure
MD.ai operates with HIPAA-compliant infrastructure, including Business Associate Agreements (BAA) for processing Protected Health Information (PHI). They enforce TLS for data in transit and AES-256 encryption at rest, operate in data centers with security certifications (e.g., ISO 27001), and maintain detailed audit logs. The platform supports robust access controls and audit logging to protect PHI. They do not use customer data to train foundation models for other customers without explicit permission.
Clinical NLP Capabilities
MD.ai's reporting suite utilizes Large Language Models (LLMs) to enhance clinical workflows, offering features like automatic template selection, key findings dictation mapping, impression generation, and proofreading. Their advanced AI capabilities include auto-comparing prior reports and applying clinical guidelines such as BI-RADS, LI-RADS, and TI-RADS. They also offer advanced chat capabilities using HIPAA-compliant LLMs for report analysis, interpretation, and answering FAQs. The platform is designed to understand medical text shorthand and abbreviations.
De-Identification Tools
MD.ai provides a suite of tools for data de-identification, including pixel-level de-identification for removing burnt-in PHI on DICOM images and DICOM tag de-identification. They use a proprietary model for detecting and classifying text on DICOM images (X-Ray, CT, MR, Ultrasound, Mammography modalities) and offer a human-in-the-loop mechanism for approving and editing de-identification outputs. De-identification works at the dataset level, creating a new dataset for each de-identification task.
Medical Terminology Support
The platform's AI Clinical Assistant uses ambient listening coupled with a leading medical dictionary designed to understand medical language. The clinical NLP capabilities are built to understand medical text shorthand and abbreviations.
Sandbox/Testing Environment
MD.ai supports customers in all stages of AI development, including model development, validation, and deployment. They enable deployment of models for AI-assisted annotation or federated validation across multiple sites without data sharing.
SDK Languages
Specific SDK languages are not explicitly detailed, but MD.ai offers flexible APIs for programmatic project management and control. They leverage Google Cloud and Cloud Healthcare API to create annotated datasets and build algorithms for machine learning, and create a direct path to AI integrations with Google Cloud via Jupyter Colab notebooks.
Rate Limits & Pricing
MD.ai's terms of service indicate that as of the effective date, it is not contemplated that the company will charge subscribers fees for access and use of the services under their agreement. However, costs and expenses incurred by the subscriber are their sole responsibility. For AI note generation, an estimated monthly cost breakdown is provided, including audio processing and AI note generation from text, with token usage estimates.
Certification Program
MD.ai itself is not described as having a certification program. However, their web-based DICOM viewer is FDA 510(k)-cleared, enabling clinical image interpretation, review, annotation, and reporting.
Physician Tip

MD.ai offers a powerful platform for radiologists and other clinicians to engage with AI. Leverage the AI-powered reporting suite for features like automatic template selection, impression generation, and proofreading to significantly boost efficiency and consistency in your daily workflow. The built-in AI tools for PHI detection and de-identification are crucial for maintaining patient privacy and compliance when working with medical imaging data. Actively participate in the AI-assisted annotation process to ensure the models are trained on high-quality, clinically relevant data, which is fundamental for accurate AI model performance. Utilize the platform's ability to compare prior reports and apply clinical guidelines to enhance diagnostic accuracy and adherence to best practices.

MD.ai is designed for seamless integration into existing healthcare IT environments. It supports HL7/DICOM integration with EHR/HIS/RIS systems. The platform can embed reporting directly into PACS, RIS, or other web applications using iframes and secure token-based authentication, streamlining workflows by eliminating separate logins. MD.ai leverages Google Cloud and Cloud Healthcare API, providing a direct path for AI integrations via Jupyter Colab notebooks. This allows for scalable and efficient management of medical imaging data and the development of machine learning algorithms.

Details

Category Developer Tools & APIs, Radiology & Imaging AI
Pricing Contact for pricing
  • Pricing is not publicly listed; typically customized based on institution scale, number of users, and features required
  • Free access to numerous public datasets is available for researchers and developers
DeploymentWeb-based, Cloud (Google Cloud Platform), On-premises, Docker-based model deployment
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

MD.ai Annotator includes an FDA 510(k)-cleared viewer for the display of DICOM and non-DICOM medical images and other healthcare data.
Integrations
EHR Not specified
Specialties Radiology

What the Web Says

MD.ai is a platform designed to accelerate AI model development in radiology, aiming to increase radiologist productivity, improve data quality for AI training, and enhance diagnostic accuracy. It offers tools for efficient annotation of medical imaging data, deployment and validation of AI models, and integration into clinical workflows. The platform has received an average overall rating of 4.7/5 based on 18 reviews, with high ratings for customer service and its ability to streamline operations.

Overall: Positive

Strengths

  • Accelerates AI model development and deployment in radiology.
  • Increases radiologist productivity by streamlining reporting workflows.
  • Improves data quality by enabling the creation of large, high-quality labeled datasets.
  • Enhances diagnostic accuracy through AI-driven features in medical imaging analysis.
  • Facilitates healthcare workflow automation and integrates smoothly with clinical systems.
  • Protects patient data with automated PHI detection and ensures compliance with privacy standards.

Limitations

  • Some AI medical scribes, not specifically MD.ai, have been criticized for lack of flexibility, limited templates, and not being robust enough for complex cases.
  • Concerns exist about the accuracy and nuance of AI in diagnostic reasoning, with some physicians finding current AI tools largely useless in their field for direct diagnostic value.
  • The quality of output from some AI doctor applications has been described as poor and potentially irresponsible, with a risk of factual errors or missing critical medical nuances (AI 'hallucination').
  • Slow feature addition and public roadmaps full of 'coming soon' items have been noted for some AI scribe platforms.
  • Some users express concerns about AI tools, particularly browser plugins, accessing non-clinical web browsing history.
  • Onboarding processes for some healthcare IT solutions, not specifically MD.ai, can be challenging with limited support.

Based on reviews from: md.ai Reviews - Read Customer Reviews of Md.ai, MD AI Products | Read 0 Reviews on G2, I tried all the AI medical scribes so you don't have to - my honest review - Reddit, I tried a lot of AI medical scribes so you don't have to - my honest review : r/medicine - Reddit, Talked to a free online AI doctor. What do you guys think? - Reddit, AI Overview Test Group - Verified Ratings & Reviews, Best AI medical research assistants 2026: MD-reviewed - MedAI Verdict, AI Doctor vs Real Doctor: How Do They Compare? (2026) - Doctronic, AI Models Are Thinking Like Patients When Evaluating Doctors - Ratings MD Blog, I work in healthcareu2026AI is garbage. : r/artificial - Reddit, Module MD Software Pricing, Alternatives & More 2026 | Capterra, As an M.D, here's my 100% honest opinion and observations/advices about using ChatGPT - Reddit, AdvancedMD EHR Reviews 2026. Verified Reviews, Pros & Cons | Capterra, Why Some Doctors Are Recommended by AI (And Others Aren't), TotalMD Reviews 2026. Verified Reviews, Pros & Cons | Capterra, AI in healthcare: Separating fact from fiction | WebMD Ignite, Capterra Reviews 2026: Details, Pricing, & Features - G2, G2: Details, Reviews, Pricing, & Features - CheckThat.ai, Study Finds More Than 26% of G2 Reviews Are AI-Generated Since Launch of ChatGPT, MDWare Software Pricing, Alternatives & More 2026 | Capterra, AI in Credentialing - MD-Staff, MD clinician exploring clinical AI / health IT: where do physicians actually add value early on? : r/healthIT - Reddit, r/EvenRealities - Reddit, REVIEWS.md: Teach Your AI Code Reviewer Your Standards - YouTube, mdhub: AI-Operating System for Behavioral Health Clinics, AdvancedMD Reviews 2026: Details, Pricing, & Features - G2, Working at MD Health: Employee Reviews | Indeed.com, Read Customer Service Reviews of interviewmd.ai - Trustpilot, A Complete Guide To AGENTS.md - AI Hero, Ai ruined mr reviews : r/softwareengineer - Reddit

Last updated: 2026-09-12

Ratings & Reviews

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Press & Coverage

Imaging Technology News
NewVue.ai, MD.ai Announce Integration Partnership
NewVue.ai and MD.ai have partnered to integrate MD.ai's structured reporting platform into NewVue's EmpowerSuite, allowing radiologists to review AI outputs, access clinical context, view images, and create reports within a single workspace. This integration aims to streamline radiology workflows, improve reporting consistency, and enhance AI-readiness.
2025-06
MD.ai White Papers & Publications
Evaluating GPT-4V (GPT-4 with Vision) on Detection of Radiologic Findings on Chest Radiographs
This study, published by the MD.ai team, examined GPT-4 with vision (GPT-4V) for detecting radiologic findings on chest radiographs, suggesting that GPT-4V is not yet ready for real-world diagnostic use.
unknown
MD.ai White Papers & Publications
Validating AI Models Collaboratively with NVIDIA Clara Imaging and MD.ai
This publication highlights how MD.ai and NVIDIA Clara Imaging can be used together to quickly deploy and validate medical imaging models.
unknown
MD.ai White Papers & Publications
Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges
This article discusses the complexities of creating and curating datasets for medical imaging AI competitions, such as those by the RSNA, emphasizing patient privacy, data quality, and consistency.
unknown
MD.ai
Google Cloud Platform Case Study
MD.ai leverages Google Cloud and Cloud Healthcare API to create annotated datasets and build machine learning algorithms for medical insights.
unknown
American Academy of Ophthalmology
The MD and AI: Harnessing New Tools for Our Patients
Stephen D. McLeod, MD, discusses the shift from the information age to the age of artificial intelligence, highlighting AI's potential to improve healthcare quality, access, and affordability.
2026-05
ResearchGate (Journal of Health Science and Report)
AI vs. MD: The Future of the Clinical Encounter
This paper examines the integration of AI into clinical practice, exploring its promise in diagnostics and treatment, as well as the perils of algorithmic bias and data privacy.
2025-12
MD+DI (Medical Device and Diagnostic Industry)
How Is FDA Regulating AI Medical Devices in 2026?
Suzanne Levy Friedman, an FDA medical device attorney, discusses the FDA's regulatory plans for medical devices incorporating AI components.
2026-09

Videos

Product demos, reviews, and walkthroughs for MD.ai.

View all on YouTube

Frequently Asked Questions

MD.ai provides a comprehensive platform for physicians and developers to build, annotate, and manage medical imaging datasets for AI model training. It includes tools for collaborative annotation, data versioning, and integration with popular deep learning frameworks, streamlining the development workflow for healthcare AI applications.
MD.ai is designed with a strong emphasis on data security and regulatory compliance. The platform incorporates features such as robust access controls, data encryption, audit trails, and de-identification tools to help users maintain HIPAA and GDPR compliance when working with sensitive patient information.
While MD.ai offers a specialized and integrated platform, alternatives exist depending on specific needs. Physicians and developers might consider open-source annotation tools combined with cloud-based machine learning platforms (like AWS SageMaker or Google Cloud AI Platform) for more custom solutions, though this often requires more manual integration and setup.
MD.ai typically offers tiered pricing models that can accommodate various user types, from individual researchers to larger institutions. Specific pricing details often depend on factors like data storage needs, number of users, and advanced features required. It's recommended to contact MD.ai directly for a personalized quote.
While powerful, MD.ai's limitations can include the need for a certain level of technical proficiency to fully leverage its developer tools, especially for complex model deployment. Additionally, the quality and quantity of available training data remain crucial, and the platform itself cannot overcome inherent biases or limitations in the input data.
MD.ai is designed with integration capabilities in mind, often supporting APIs and standard protocols to facilitate data exchange. While direct, out-of-the-box integration with every PACS or EHR system may vary, the platform generally provides mechanisms to connect and import relevant medical imaging and patient data.
MD.ai primarily focuses on providing the tools and infrastructure for building and training custom AI models. While it may offer some baseline models or examples, its core strength lies in empowering users to develop their own specific AI solutions rather than providing a library of off-the-shelf, pre-trained clinical AI models.

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Suggest an Edit → | Last Verified: 2026-06-11 | First Added: 2026-06-11
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