Physician-led annotation infrastructure for clinical datasets. Structured reasoning traces, not just labels.
Fabrica Annotation connects your clinical datasets with board-certified physicians who label, classify, and annotate your data with structured reasoning. Every annotation captures not just the clinical decision, but the evidence considered, alternatives weighed, and the logic behind the final judgment.
The output is training-ready data for supervised learning, model evaluation, and research publication — with full provenance tracking for every label.
Upload raw clinical data or pull directly from cloud databases on AWS and GCP — patient records, imaging data, lab results, clinical notes, discharge summaries. Data is de-identified before entering the annotation environment.
Define your annotation schema with our team — the labels, reasoning dimensions, and output format your downstream models need. We run calibration batches to validate the schema before scaling.
Qualified physicians across our network independently review each record. They provide primary labels, supporting evidence, reasoning chains, confidence levels, and alternative considerations. Disagreements go through structured adjudication — or are preserved as uncertainty signal, depending on your needs.
Get verified, structured datasets with quality metrics, inter-annotator agreement scores, and full audit trails. Export in the format your training pipeline expects.
Not just labels — structured records of the clinical thinking behind every annotation. Evidence cited, differentials considered, confidence expressed.
We help you define annotation schemas that capture the clinical signal you need without introducing noise. Includes calibration rounds and iterative refinement.
Inter-annotator agreement (Fleiss' kappa, Cohen's kappa), label distribution reports, and per-record confidence scores. You see exactly how consistent and reliable your annotations are.
De-identification, role-based access controls, encrypted data at rest and in transit, full audit logging. Designed for clinical data from the ground up.
Build supervised learning datasets from unstructured clinical notes — diagnosis classification, clinical entity relationships, treatment outcome labeling.
Generate training data for models that assist with differential diagnosis, risk stratification, or treatment recommendations — tasks that require expert reasoning, not just pattern matching.
Produce annotated datasets for academic research with the inter-annotator agreement metrics and provenance tracking that peer review demands.
See how annotation fits into the broader clinical AI data pipeline in our Clinical Data Annotation guide.
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