Evaluate Healthcare AI Beyond a Single Accuracy Number
SCILabel supports structured evaluation of healthcare AI systems using clinically relevant datasets, expert review and task-specific evaluation criteria.
Evaluation should reflect the intended clinical task
The appropriate metrics and review methods depend on the model type, target population, modality, use case and potential failure modes.
Performance Testing
Assess model outputs using appropriate reference data and task-specific metrics.
Clinical Review
Use domain experts to assess clinical correctness, relevance and usefulness where human judgment is required.
Subgroup Analysis
Evaluate performance across defined cohorts or dataset characteristics where data permits.
Failure Analysis
Characterize false positives, false negatives and other meaningful error patterns.
Robustness
Test defined variations in data quality, source or scenario where appropriate.
Validation Reporting
Structure findings so development teams can understand observed strengths and limitations.
Build your healthcare AI data workflow with SCILabel
Talk to our team about your healthcare data, annotation or AI evaluation requirements.