Data annotation for
medical devices

From next generation AI-enabled devices to clinical decision support, Centaur Labs provides medical device innovators with high-quality medical data annotations at unprecedented speed and scale.

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

Use cases

Accelerating development of intelligent medical devices with leading data annotation capabilities

Clinical decision support

AI allows providers to leverage big data at the bedside, supporting decisions throughout the care continuum, and driving improved patient outcomes.

Image analysis

Assisted diagnosis

AI-enabled surgery

Teams are building AI to help make complex, time-sensitive diagnoses, increase the accuracy and efficiency of surgery, and prevent postoperative complications.

Robotic surgery

AR/VR surgery simulation

Patient monitoring

Physicians, caregivers and patients can make care decisions based on both patient-reported symptoms and data streams from next generation medical devices.


Connected devices

Disease management software

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


Eko Health uses Centaur Labs to annotate lung sounds for its AI-enabled digital stethoscope that detects heart and lung abnormalities.


Current labeling process was manual, time intensive and taxing on their team. Unscalable labeling process was slowing them down. Wanted to label more quickly without sacrificing quality.


Centaur Labs delivered 120,000 expert opinions in 2 weeks for a dataset of 20K+ lung sound audio recordings. Classified each recording as either crackle, weeze, rhonchi, or cough.


AUC improved to .92 with dataset labeled by Centaur Labs, from .87 with original dataset.

Ready to accelerate your AI development?