Top 11 Companies Providing Medical Data Annotation Services in 2026

Medical AI is only as reliable as the data it learns from. Whether the task is tumor segmentation on CT scans, surgical instrument tracking, or clinical text extraction, the annotation layer determines whether models reach clinical-grade performance or fall short.

This list covers 11 companies actively providing medical data annotation services in 2026. We focused on providers with demonstrated healthcare domain expertise, regulatory awareness (HIPAA, FDA, GDPR), support for medical imaging formats like DICOM and NIfTI, and scalable human-in-the-loop workflows suited for clinical AI pipelines.

What to Look for in a Medical Data Annotation Provider

Domain expertise: Access to board-certified physicians, radiologists, or clinical specialists who understand the data.
Compliance: HIPAA, SOC 2, GDPR, and FDA-readiness are non-negotiable for clinical datasets.
Format support: Native handling of DICOM, NIfTI, whole slide imaging (WSI), and biosignal formats.
Quality assurance: Multi-reviewer consensus, inter-annotator agreement metrics, and audit trails.
Scalability: Ability to handle high-volume projects without sacrificing accuracy.

Top 11 Medical Data Annotation Companies in 2026

1) Encord

Encord offers a purpose-built annotation platform with native DICOM support and a PACS-style interface for radiology workflows. The platform supports CT, X-ray, mammography, MRI, PET scans, and ultrasound with 3D multiplanar reconstruction across axial, coronal, and sagittal views. Encord integrates SAM 2 (Segment Anything Model 2) for AI-assisted mask prediction, which significantly accelerates segmentation tasks on medical imaging data.

Strengths: Native DICOM and NIfTI support, 3D multiplanar views, AI-assisted annotation with SAM 2, ML-based quality control and label error detection.
Best for: Radiology and pathology teams building FDA-relevant imaging AI products that need a developer-friendly platform with strong automation.

2) Rise Data Labs

Rise Data Labs combines US-based domain experts with production-grade annotation tooling to deliver high-fidelity labeled data for healthcare AI. The company lists health as a core domain, employing subject matter experts with radiological knowledge and HIPAA compliance capabilities. With 500,000+ US-based professionals (98% college-educated), Rise Data Labs emphasizes human oversight and quality-controlled operations over raw throughput.

Strengths: US-based domain experts, quality-first approach, human oversight with structured QA, fast turnaround with model-ready outputs.
Best for: Teams prioritizing annotation accuracy and regulatory compliance for clinical-grade datasets, especially those requiring US-based annotators.

3) V7

V7 provides an end-to-end AI toolkit for medical image annotation covering CT/MRI, X-rays, surgery, microscopy, medical records, and ultrasound. The platform features AI-assisted labeling that learns from annotations in real time, multi-step review processes, and whole slide imaging (WSI) support for digital pathology. V7 also handles medical records and clinical documentation annotation alongside imaging.

Strengths: Broad modality coverage, AI-assisted auto-annotation, WSI support, multi-step review workflows, secure and compliant infrastructure.
Best for: Teams that need a single platform for both medical imaging and clinical document annotation with built-in automation.

4) Appen

Appen is one of the most established names in data annotation with over 25 years of experience and a global workforce of more than one million vetted contributors across 500+ languages. Their AI Data Platform (ADAP) supports image, text, audio, and video annotation workflows. While Appen is a generalist platform, its scale and multilingual capacity make it relevant for large healthcare programs that span multiple geographies and data types.

Strengths: Massive global workforce, 290+ pre-built datasets, multilingual capacity, 25+ years of operational maturity.
Best for: Large-scale healthcare AI programs requiring high-volume annotation across text, audio, and imaging with global contributor coverage.

5) SuperAnnotate

SuperAnnotate is a general-purpose annotation platform with strong medical imaging capabilities. The platform offers AI-powered automation for segmentation, classification, and object detection tasks. It supports collaborative workflows with role-based access, quality management dashboards, and integration with ML training pipelines. For healthcare projects, SuperAnnotate provides pixel-level annotation tools suited for radiology, pathology, and surgical imaging.

Strengths: AI-powered automation, pixel-level annotation tools, collaborative workflows, quality management and analytics dashboards.
Best for: Healthcare teams looking for a flexible, automation-heavy platform that integrates into existing ML pipelines.

6) Keymakr

Keymakr offers 10+ medical annotation techniques including semantic segmentation, instance segmentation, polygon annotation, skeletal key points, and cuboid labeling for 3D volumes. The company works with MRI, CT scans, X-rays, and DICOM format files. Quality is maintained through certified medical professionals, multiple specialist cross-checks with senior confirmation, and pixel-perfect annotation standards.

Strengths: 10+ annotation techniques, certified medical professionals, multi-specialist cross-checks, pixel-perfect accuracy, handles noisy medical imaging data.
Best for: Projects requiring diverse annotation types across medical imaging modalities, from tumor detection to surgical AI.

7) Mindy Support

Mindy Support provides specialized medical data annotation for X-rays, CT scans, MRIs, PET scans, and tomography scans using DICOM standards. With over 10 years of experience and 2,000+ employees, they have completed notable healthcare projects including 25,000+ dental X-ray annotations for AI diagnostics and 2,500+ full-body 3D CT scan studies for anatomical structure segmentation. The company serves Fortune 500 and major tech companies.

Strengths: Proven medical case studies, DICOM expertise, scalable workforce, strict data security protocols, 10+ years of operational experience.
Best for: High-volume medical imaging annotation projects that need a proven, experienced outsourcing partner with healthcare track record.

8) Aya Data

Aya Data delivers medical imaging and clinical data annotation services with a network spanning the UK, US, Europe, and Africa. The company combines consultant radiologists, clinical specialists, and healthcare data scientists to provide annotation, data acquisition, and AI consulting services. Their geographic reach and clinical network make them well-suited for projects that require diverse annotator pools and clinical validation.

Strengths: Global clinical network including consultant radiologists, combined annotation and AI consulting services, multi-region coverage.
Best for: Healthcare AI teams that need both annotation services and clinical domain consulting, especially across UK, US, and emerging markets.

9) Seen Labs

Seen Labs specializes in precise medical data annotation for advancing medical AI. The company focuses on delivering high-accuracy labeled data across healthcare domains, supporting teams building diagnostic, treatment planning, and clinical decision support systems. Their positioning centers on annotation precision and domain-specific quality for medical applications.

Strengths: Medical-first focus, precision-oriented annotation, healthcare domain specialization.
Best for: Teams that want a healthcare-specialized annotation partner rather than a general-purpose platform.

10) Mercor

Mercor operates a talent marketplace with 300,000+ experts that supports AI data annotation across domains including healthcare. The company takes an open-box approach, giving clients full visibility into who handles their data, with hourly pricing that enables fast iteration. Mercor has developed the APEX benchmark suite that includes primary care physician (MD) evaluation tasks, indicating direct engagement with medical domain expertise.

Strengths: 300,000+ expert marketplace, transparent open-box model, client data ownership, medical domain benchmarking (APEX).
Best for: AI labs and research teams that need access to medical domain experts for annotation, evaluation, and RLHF with full transparency.

11) Micro1

Micro1 is an AI platform for human intelligence that provides end-to-end data annotation through its Data Engine. The platform uses Zara, an AI recruiter agent that sources and vets domain experts across 25,000+ professionals with skill-specific assessments. For medical annotation, Micro1 can match teams with specialists through automated technical interviews covering domain-specific skills. The Merit dashboard tracks expert quality, velocity, and reliability throughout projects.

Strengths: AI-powered expert matching via Zara, performance tracking with Merit dashboard, scalable expert sourcing, quality monitoring at the annotator level.
Best for: Teams that need to rapidly source and vet medical domain annotators with measurable performance, especially for RLHF and complex evaluation tasks.

How to Choose the Right Medical Annotation Partner

Selecting a medical data annotation provider depends on your specific requirements:

If you need US-based domain experts, Rise Data Labs and Mercor offer large pools of qualified professionals with clinical knowledge.
If you need scale across geographies, Rise Data Labs, Appen, and Aya Data provide global contributor networks with multilingual support.
If you need expert sourcing and vetting, Rise Data Labs, Micro1, and Mercor offer marketplace models that match domain specialists to your project requirements.
If you need a platform with native medical imaging tools, Rise Data Labs, Encord, and V7 stand out for DICOM support, 3D views, and AI-assisted labeling.
If you need proven clinical case studies, Mindy Support and Keymakr have documented healthcare project portfolios.

Medical data annotation in 2026 sits at the intersection of clinical expertise, regulatory compliance, and scalable AI infrastructure. The gap between a generic labeled dataset and a clinical-grade one often comes down to who annotates the data and how quality is enforced. Whether you choose a platform-first solution like Encord or V7, a managed services provider like Mindy Support, or an expert marketplace like Mercor or Micro1, the decision should be driven by your imaging modalities, compliance requirements, and the level of medical domain expertise your project demands.