The AI data platform built for healthcare teams
From surgical video and DICOM imaging to clinical notes and EHR documents, Encord gives enterprise healthcare organisations a single, compliance-ready platform for AI training data at scale.
“Encord is essential in our daily activities because it allows us to focus on model optimisation and testing.”
Why leading healthcare teams are switching to Encord
Purpose-built for the scale, security, and compliance requirements of large healthcare organisations.
One platform for every modality you work with
DICOM imaging, surgical video, clinical notes, PDFs, and audio. Encord handles all of them natively, in a single workspace.
One platform for every modality you work with
DICOM imaging, surgical video, clinical notes, PDFs, and audio. Encord handles all of them natively, in a single workspace.
Built to scale, from pilot to production
Other tools and open-source tools work for experiments. But, they collapse under enterprise annotation programmes: no scalable workforce management, no QA workflows, no active learning, no programmatic access. Encord grows from 5 to 1,000+ annotators without requiring you to rebuild your stack.
FDA-ready traceability, without extra work
Encord provides audit trails and logs of key actions and tracks user interactions and changes. Consensus workflows and exportable audits ensure regulatory submissions with traceability documentation.
FDA-ready traceability, without extra work
Encord provides audit trails and logs of key actions and tracks user interactions and changes. Consensus workflows and exportable audits ensure regulatory submissions with traceability documentation.
Researchers at KCL annotated endoscopy videos of pre-cancerous polyps using Encord. Employing clinicians for this work is expensive and time-consuming. Speed without sacrificing accuracy was critical. Using Encord's object tracking and micro-model functionality, annotators labeled a small number of frames, then let a trained model predict the rest.
Read the full case studyFaster video annotation vs CVAT
Improvement in senior clinician labeling efficiency
Model development time cut by 10 months
Our additional resources for healthcare AI Teams.
Frequently asked questions
Yes. Encord offers full VPC deployment on AWS, GCP, or Azure. Your data stays entirely within your own cloud perimeter. No patient data o processed on Encord's infrastructure. We support BYOC configurations for stricter data residency requirements and provide full documentation for security review processes.
Yes to both. Encord is HIPAA-ready and signs Business Associate Agreements (BAAs) as part of enterprise contracting. We support de-identification workflow and role-based access for protected health information. We are also SOC 2 Type II certified and GDPR compliant, with full security documentation available for your procurement review.
Yes, Encord natively supports DICOM, surgical video, clinical notes and EHR documents, PDFs and audio data all within a single platform, with a shared workflow engine, unified audit trail, and consistent role-based access across all data types.
Yes. Encord is model-agnostic. Connect your own models via API to generate pre-labels, surfaced to human reviewers for correction and approval. You can also use Encord's built-in SAM-3 for segmentation tasks, or run agent-based pre-labelling for document and text workflows.
Metrics are calculated automatically across reviewers, providing documented evidence of annotation quality. This directly supports requirements and the data governance section of FDA submissions.
Configure multi-reviewer consensus, requiring two or more reviewers to agree before a label is accepted, and set thresholds that automatically flag disagreement for adjudication. QA surfaces low-confidence annotations for human review without checking everything manually. The interface minimises clicks for time-constrained domain experts and can integrate into existing clinical routines rather than requiring a separate platform login.
Encord supports import from COCO, YOLO, JSON, and CSV formats and can ingest existing label schemas to preserve work already completed. Most teams run a parallel pilot on one project before migrating with a dedicated solutions engineer.