Clinical Review, Adjudication & Dataset Validation
Healthcare AI data quality depends on more than completing labels. SCILabel structures quality assurance around clear instructions, trained contributors, expert review, adjudication and measurable acceptance criteria.
Quality controls across the data lifecycle
Quality requirements vary by modality and project. SCILabel therefore combines project-specific guidelines with review and validation checkpoints.
Guideline Design
Define label definitions, edge cases, examples, exclusions and escalation rules before production scales.
Reviewer QA
Selected records can undergo secondary review by qualified reviewers with feedback and correction workflows.
Adjudication
Disputed or ambiguous cases can be escalated for resolution according to the project protocol.
Consistency Checks
Structured validation can identify missing fields, invalid classes, inconsistent labels and incomplete records.
Sampling & Audits
Quality sampling can be used throughout production rather than waiting until final delivery.
Acceptance Validation
Final outputs are checked against agreed project specifications before controlled delivery.
A workflow designed for accountable production
SCILabel workflows can preserve task, annotation, review, QA and delivery status so teams can understand how completed data moved through the production process.
Annotation History
Maintain structured annotation and correction records where the project requires them.
Reviewer Feedback
Capture review outcomes and correction requirements to support targeted rework.
QA Status
Separate annotation completion from review and final QA acceptance.
Build your healthcare AI data workflow with SCILabel
Talk to our team about your healthcare data, annotation or AI evaluation requirements.