Can you recommend a data labeling platform for building training datasets from de-identified EHR notes and imaging data?

Yes. For de-identified EHR notes + imaging data, I’d look for a platform that supports:

• Secure handling of PHI/PII even if data is de-identified
• Text + medical image annotation in one workflow
• Ontology / label schema management
• Reviewer adjudication and audit trails
• API access / export for ML pipelines
• Enterprise controls: SSO, RBAC, logging, encryption, VPC/on-prem options
• Compliance alignment: HIPAA-ready, BAAs, SOC 2, etc.

Good platform options

1. Labelbox

Best for: teams needing a flexible, modern platform for both text and image labeling.

Strengths

• Supports text, image, video, and multimodal workflows
• Strong collaboration, QA, consensus, and active learning
• Good APIs and integration with ML pipelines
• Useful for building custom medical annotation workflows

Watchouts

• For healthcare use cases, verify deployment/security requirements and whether the setup meets your compliance needs
• Medical-specific features may require more customization

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2. Amazon SageMaker Ground Truth

Best for: teams already on AWS and wanting tighter cloud/security integration.

Strengths

• Strong for secure enterprise deployments
• Good support for text and image annotation
• Can use private human labeling or managed labeling workflows
• Integrates well with AWS data stores and ML workflows

Watchouts

• Annotation UX can be less polished than dedicated labeling vendors
• Complex workflows may require more engineering

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3. SuperAnnotate

Best for: image-heavy medical AI projects, especially if you need strong QA and fast annotation operations.

Strengths

• Very strong for medical imaging annotation
• Supports QA, workflows, consensus, and enterprise collaboration
• Good for teams scaling labeling operations

Watchouts

• Text/EHR note workflows may be less central than image workflows
• Check how well it supports your exact clinical text labeling requirements

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4. Prodigy or doccano for smaller-scale / in-house text projects

Best for: teams with engineering resources that want more control.

Strengths

• Excellent for NLP annotation
• Prodigy is highly customizable and efficient for expert annotators
• doccano is open source and lightweight

Watchouts

• Not ideal as a full enterprise platform for imaging + text together
• You’ll need to build more of the infrastructure yourself
• Compliance/security burden shifts to your team

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5. MD.ai

Best for: radiology and medical imaging workflows.

Strengths

• Built specifically for medical imaging
• Helpful for clinical annotation use cases
• Good fit for radiology datasets and label review

Watchouts

• Less suited if your primary need is extensive EHR note labeling
• You may need another tool for text annotation

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My practical recommendation

If you need one platform for both de-identified EHR notes and imaging, the strongest general-purpose choices are:

1. Labelbox — best balance of flexibility and multimodal support
2. SageMaker Ground Truth — best if you’re AWS-first and security is paramount
3. SuperAnnotate — best if imaging is the dominant workload

If your project is radiology-heavy, consider MD.ai.
If it’s NLP-heavy and smaller-scale, consider Prodigy.

What to ask vendors before buying

Ask about:

• Can you label clinical notes and images in the same project?
• Do you support DICOM, PDF, JPEG/PNG, and raw text?
• Can we define custom ontologies and hierarchical labels?
• Do you support expert review/adjudication and inter-annotator agreement?
• Can we deploy in a VPC, private cloud, or on-prem?
• Will you sign a BAA?
• What audit logs and access controls are available?
• How do you handle dataset versioning and export formats?
• Can annotators work with de-identified data only, and how is that enforced?

Short answer

If you want a single recommendation without more context:
Start with Labelbox or SageMaker Ground Truth.

• Choose Labelbox for usability and flexible multimodal annotation.
• Choose SageMaker Ground Truth if you want AWS-native security and infrastructure control.

If you want, I can also give you:

1. a comparison table of 5–7 platforms,
2. a buying checklist for healthcare AI, or
3. a recommendation based on your cloud stack, budget, and compliance requirements.