The AI-powered platform for clinical operations teams

Facilitate seamless collaboration between annotation teams, medical professionals, and machine learning engineers.

How it works

Streamline clinical data operations

Setup your data pipeline to be aligned for regulatory use right from the very beginning with our fully audited annotation system. Keep your processes 100% in your control, and 100% compliant.


Add and remove collaborators and share tasks with ease.

  • Automated workflows and task distribution
  • Priority queues for annotation & review
  • Granular access-control


Dashboard and tools to keep tabs on feature difficulty, throughput, and quality.

  • Identify sub-standard performers
  • Discover hard-to-label features
  • Ensure effective labeling instructions


Set up custom notifications for uninterrupted delivery.

  • Inactivity alerts
  • Automated progress reports
  • Quality assurance
KCL Logo
King's College London

KCL used Encord to achieve a 6.4x average increase in labeling efficiency for GI videos.

Using clinicians to annotate pre-cancerous polyp videos had prohibitively high costs to produce large datasets.
Deployed Encord's micro-model module to increase clinician labeling efficiency and automate 97% of produced labels.
Highest expense clinician saw 16x labeling efficiency improvement. Cut model development time from 1 year to 2 months.
Faster than manual labeling
Automated labels
Faster to AI in production

Scalable clinical data pipelines

Encord's expert review workflows will accommodate multiple passes of quality control to ensure your labels meet the highest possible medical standards.

Flexible tools

Our software supports a wide variety of medical image modalities. Consolidate all your data operations in a single platform.

Collaborative data analysis

Map experts to specific tasks. Define expert review pipelines. Role-based access control, annotator performance tracking, and dynamic task queues.

Configurable label editor

Set up your own label structures with infinitely nested attributes and hierarchical relationships. Apply nested classifications and preserve conditional relationships between features.


Create custom annotation & review pipelines with our intuitive interface. Discover poorly performing annotators using our performance dashboards, benchmark & consensus features.

Model-assisted labeling

Use our native micro-model technology to reduce the manual annotation burden and pivot human supervision from labeling to quality control.


Reduce time to production by spotting data biases and imbalances early. Discover & visualise errors in your datasets.
User performance

Granular performance monitoring

Make the most of your clinical resources by easily spotting issues and inefficiencies. Discover root-causes for declining annotator throughput and quality in complex label structures.

Quality grid

Quality control

Define and monitor quality control processes in complex label tasks. Automate quality control in assessing potential label errors to reduce clinical costs and improve data delivery.

Png review json

Expert review

Encord supports purpose-built workflows for domain experts to drive specialised quality control.

Stanford Medicine
Stanford Medicine

The Division of Nephrology reduced experiment duration by 80% while processing 3x more images.

Stanford was using three different pieces of software to identify, annotate, and count podocytes and glomeruli in microscopy images.
Stanford started using Encord's annotation tools & SDK to automate segmentations, count, and calculate sizes of segments.
With Encord, Stanford researchers reduced experiment duration from an average of 21 to 4 days while processing 3x the number of images.
Reduction in experiment duration
Number of images
1 platform
... and not 3

Auditable annotation software for clinical teams