Company Overview
About Centaur Labs
Centaur Labs is a medical data annotation company headquartered in Boston, Massachusetts, founded in 2018 by Erik Duhaime and others. The company has built a platform that produces high-accuracy medical AI training data by aggregating annotations from a curated crowd of medical professionals — physicians, nurses, medical students, and other clinicians — rather than relying on individual expert annotation or general-purpose crowdsourcing. Its core insight is that combining many clinician judgments on ambiguous medical cases produces annotations that are as accurate as (or more accurate than) single expert opinions, while being dramatically faster and more scalable.
Business Model & Competitive Advantage
The Centaur Labs platform covers a broad range of medical data modalities: medical imaging (radiology, pathology, dermatology), audio diagnostics (heart and lung sounds), clinical text, and waveforms (ECG, EEG). Medical AI companies and healthcare organizations use Centaur to create training datasets for diagnostic AI models — a task that requires domain expertise that general-purpose annotation vendors (Scale AI, Labelbox, Appen) cannot provide reliably for medical applications. The platform gamifies the annotation task through a mobile app called DiagnosUs, which medical professionals use to earn compensation by labeling medical cases during spare moments.
Competitive Landscape 2025–2026
Centaur Labs raised a $30M Series B in 2023 with investors including Osage University Partners and other healthcare-focused funds. Customers include medical AI developers across radiology, cardiology, dermatology, and pathology — companies building diagnostic AI that requires large volumes of expert-annotated training data and validation datasets. The company competes with general annotation platforms (Scale AI, Labelbox) that have added healthcare-specific annotation services, as well as with health system-affiliated annotation services and individual medical expert annotation programs. Centaur Labs' differentiation lies in the scale of its medical professional crowd, the speed of annotation delivery, and the statistical validation of annotation quality through its collective intelligence methodology.
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Estimated Visibility Trend (Beta)
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