Top 5 Data Annotation Platforms in Healthcare (2025 Edition)

In the age of AI-powered diagnostics, data annotation is the fuel driving the accuracy and reliability of healthcare algorithms. Whether you’re building models for radiology, pathology, surgical robotics, or dermatology, high-quality annotation is critical — and choosing the right annotation platform can significantly impact the speed and success of your AI development.

At medDARE, we work daily with AI developers, startups, and health tech companies to deliver expertly annotated datasets. We’ve tested and used many platforms across hundreds of projects. In this article, we’ve selected the top 5 medical data annotation platforms that we consider the most effective, flexible, and widely adopted in healthcare AI.

1. RedBrick.AI

RedBrick.AI is a cloud-based annotation platform built specifically for medical imaging. It supports DICOM-native workflows and integrates well with clinical image formats like CT, MRI, and X-ray. Features like smart annotation tools, multi-user collaboration, and structured labeling templates make it a go-to choice for regulated environments.

Why we like it at medDARE:

• Ideal for projects that require radiologist-in-the-loop annotation
• Efficient segmentation tools and 3D volume rendering
• Supports audit trails and compliance with HIPAA/GDPR

✅ medDARE uses RedBrick.AI in many client projects requiring multi-layer review and adjudication by certified radiologists.

2. 3D Slicer

3D Slicer is an open-source platform that has been widely used for segmentation and visualization in radiology and research. It offers powerful extensions for multi-modality analysis, and is especially useful in neuroimaging and surgical planning contexts.

Why we like it at medDARE:

• Powerful for manual segmentation of anatomical structures
• Highly customizable with Python scripting
• Trusted by academic and clinical research communities

✅ medDARE teams use 3D Slicer for projects involving bone, organ, and tumor segmentation on CT and MRI scans.

3. V7 Darwin

V7 Darwin combines AI-assisted annotation with a user-friendly interface. It’s particularly good for 2D and 3D image labeling, with automated tools that speed up annotation using deep learning. It’s widely adopted for both medical and general computer vision use cases.

Why we like it at medDARE:

• Excellent for fast annotation of large volumes
• Automation tools reduce time spent on repetitive labeling
• Great for dermatology and pathology image annotation

✅ We’ve used V7 Darwin at medDARE for high-throughput dermatology image segmentation and quality control workflows.

4. Encord

Encord is a platform designed for complex medical imaging workflows, including 3D volumetric labeling, instance segmentation, and collaborative reviews. It also offers features for active learning, which can help optimize datasets by prioritizing the most informative samples.

Why we like it at medDARE:

• Smart labeling suggestions based on AI models
• Easy integration into ML pipelines
• Designed for regulated, clinical-grade datasets

✅ At medDARE, we recommend Encord for multi-step annotation pipelines where consistent, high-quality segmentation is essential.

5. ITK-SNAP

ITK-SNAP is a specialized open-source tool focused on manual and semi-automatic segmentation of 3D medical images. While less cloud-friendly than others, it’s extremely precise and highly respected in clinical research.

Why we like it at medDARE:

• Great for manual tumor segmentation
• Precision and control over voxel-level annotation
• Open-source and widely used in academic publications

✅ We use ITK-SNAP at medDARE for oncology-focused segmentation tasks, especially where high accuracy is required.

Choosing the Right Platform: What Matters Most?

When selecting a medical data annotation platform, consider the following:

• Type of modality (CT, MRI, X-ray, pathology, ultrasound)
• Clinical vs. non-clinical users involved
• Scalability and automation capabilities
• Security and compliance with healthcare regulations
• Workflow complexity and review requirements

At medDARE, we’re platform-agnostic — we work across RedBrick, V7, Encord, 3D Slicer, ITK-SNAP, and even internal client tools. Our goal is to help you deliver AI-ready annotated datasets with accuracy, speed, and clinical insight.

📈 Ready to Accelerate Your Healthcare AI Project?

Whether you need expert annotation, custom data collection from real-world clinics, or U.S.-certified radiologist reviews, medDARE is here to help.

Let’s build AI that clinicians can trust — starting with the data.