Clinical Expertise Where Healthcare Data Needs Human Judgment
SCILabel supports healthcare AI projects with clinical review, annotation verification, adjudication and quality assurance by appropriately matched domain professionals.
Review designed around the clinical task
The required reviewer profile depends on the modality, specialty, project risk and expected level of clinical interpretation.
Annotation Review
Verify labels against project definitions and available source information.
Adjudication
Resolve disagreements or ambiguous records according to the project protocol.
Clinical Relevance
Assess whether outputs are meaningful and appropriate for the intended clinical context.
Error Analysis
Identify recurring annotation or model-output failure patterns.
Escalation
Route difficult cases to an appropriate review level where required.
Final QA
Validate completed outputs against defined acceptance criteria.
Build your healthcare AI data workflow with SCILabel
Talk to our team about your healthcare data, annotation or AI evaluation requirements.