Data annotation for

From AI innovation in emergency medicine to cardiology, Centaur Labs provides researchers on the cutting edge of their fields with high-quality medical data annotations at unprecedented speed and scale.

Brigham and Women's Hospital
Memorial Sloan Kettering Cancer Center
Memorial Sloan Kettering Cancer Center
Brigham and Women's Hospital
Memorial Sloan Kettering Cancer Center
Massachussetts General Hospital

Use cases

Advancing medical and biomedical research with cutting edge data annotation capabilities.

Clinical decision making

Researchers are exploring ways to improve clinical decision making, whether through improved skill assessment and development, or through AI-enabled tools proven to enhance performance.

Medical education

AI-enabled clinical decision support

AI development

Increasingly AI developers and researchers are partnering to develop AI and ML algorithms that can advance the medical and life sciences.

Training datasets

Test and validation datasets

Dataset insights

Data analysis

Researchers across medical and biological disciplines are leveraging large and diverse datasets for analysis and discovery.

Normalizing ‚Äėdirty‚Äô data

Annotating features within datasets

Why Centaur Labs

Speed and scale

Access millions of annotations weekly from a network of thousands of medical experts, taking models from exploratory analysis to deployment in weeks, not months.

Quality by design

Experience how label quality is built into the Centaur Labs system, with frequent performance measurement and pay-for-performance incentives.

Advanced dataset insights

Leverage more reliable statistical information to inform your algorithm training plan.

Privacy and security

Worry less with leading privacy and security capabilities.

Health data focused

Lean on Centaur Labs’ expertise as the only annotation company focused on health, biological and scientific data.

All data types

From pathology slides to surgical video to unstructured clinical notes, get all of your annotation needs in one single platform.

Case studies


Researchers at the Massachusetts General Brigham Neurology Department.


Test if trained neurologists could accurately identify 6 common EEG findings within an expert-annotated dataset of EEGs, and if they could improve over a short time.


Neurologists attending the annual three-day Critical Care EEG Monitoring Research Consortium Conference downloaded the DiagnosUs mobile application and shared their opinions on the EEGs. The app offered instructions and feedback as neurologists shared answers.


The results revealed that experts‚Äô accuracy at identifying EEG findings improved with more exposure‚ÄĒin some classes, by over 150%.


Across three labeling tasks including malignancy classification and lesion recognition, Centaur collected over half a million opinions from its network of experts. Recent research published in the Lancet assessed how accurate board-certified experts with 10+ years experience were at this multi-class skin-lesion labeling task. The performance of the Centaur’s network without any experience exclusion criteria was more accurate (78.1%, p<0.001, vs 74.7%) than those physicians.

Ready to accelerate your AI development?