Physicians and clinical specialists review and evaluate your medical AI – inside privacy-first, compliance-ready workflows.
Talk to usThe conditions that separate a durable data operation from a one-off labeling project.
The people qualified to judge a lesion boundary or a discharge summary are practicing specialists, and their review time comes in short windows between clinical duties. The pipeline has to be built around that scarcity.
Two radiologists will draw the same lesion boundary differently, and clinicians will disagree on what counts as an error in a model-written note. The dataset needs a documented way to resolve those cases.
A regulatory submission asks who reviewed each item, with what qualifications, under which version of the instructions. Most pipelines cannot reconstruct that after the fact.
Clinicians score responses on safety and correctness rubrics, producing data for training and RLHF.
Entities, relations, and key events labeled across notes, reports, images, and structured fields.
Extraction, classification, and relation tagging for search and reasoning over medical records.
DICOM studies segmented and adjudicated by specialist radiologists, agreement tracked per class.
Guidelines first, calibration before scale, documentation at the end – the same structure for every use case.
Labeling and evaluation guidelines built with your clinical team, and a reviewer bench matched by specialty. You see the credentials before work begins.
A small batch measured first – inter-reader agreement and gold-set accuracy per reviewer. If the numbers fall short, the guidelines are fixed before scale.
Annotation and adjudication run on a standing cadence, with per-batch QA reports your data team can verify item by item.
Exportable audit trails and consensus records – who reviewed each item, under which version of the instructions.
Flo Health scaled clinician review of its health assistant on SuperAnnotate, reaching 10x evaluation throughput and 5-day iteration cycles inside its Databricks pipeline.
From the first de-identified batch to the documentation your submission cites – one dependency, clinically reviewed.
Bring one study protocolPlatform, experts, and workflows – unified in one secure infrastructure.