For medical AI teams
Clinical ground truth that stands up to regulators
Generic annotators don’t have clinical judgment. In-house physicians don’t scale. DataLaps runs credential-verified bilingual physicians doing double-blind consensus labeling — so you get a defensible agreement metric, not crowdworker guesses.
Run a measured pilot — send a sample sprint, keep the data and the results.
Built for the work generic labelers can’t do
Medical imaging & radiology
Engineers can’t label clinical findings, and crowdworkers have no radiological judgment — so your detection model inherits noisy ground truth.
DataLaps: Specialty-matched physicians label in parallel; double-blind consensus gives you a defensible inter-annotator agreement metric.
Clinical LLMs & RLHF
Your model gives answers that are plausible but clinically unsafe. Generic labelers can’t tell a correct answer from a dangerous one.
DataLaps: Verified MDs rank responses, write reference answers and flag unsafe outputs — bilingual, for models in English and Spanish.
EHR, records & trial data
You need large volumes of clinical data structured with auditable medical judgment — compliance won’t accept anonymous labelers.
DataLaps: Credential-verified physicians (license checked against official registries) + aggregate reporting with no PII for your regulatory file.
How the consensus works
Verified physicians
Every annotator is a licensed MD, credentials checked against official registries. No crowdworkers.
Double-blind consensus
Multiple physicians label independently; agreement is measured and disagreements escalate to a senior reviewer.
Defensible dataset
You receive labeled data plus a statistical agreement metric — the evidence regulators and clinical advisors ask about.
See a sample consensus dataset
Tell us your use case and we’ll show you exactly how verified physicians would produce your ground truth. We respond within one business day.
Request a demo →