Build Reliable Reference Data for Healthcare AI
SCILabel develops project-specific ground truth through structured annotation, expert review, adjudication and documented quality controls.
Ground truth is a process, not just a label
Reference datasets can require multiple stages of interpretation and agreement depending on the clinical task.
Ontology Design
Define classes, attributes, structures and edge-case handling.
Independent Annotation
Use appropriately trained annotators or experts according to the task.
Consensus
Compare or reconcile multiple interpretations where the project requires consensus.
Adjudication
Escalate disagreements to designated reviewers or specialists.
Documentation
Preserve guideline versions and relevant decisions for reproducibility.
Validation
Evaluate the completed reference set against project acceptance requirements.
Build your healthcare AI data workflow with SCILabel
Talk to our team about your healthcare data, annotation or AI evaluation requirements.