Clinical Expert Evaluation for Medical Language Models
SCILabel provides healthcare-domain evaluation workflows for clinical LLMs and generative AI, including expert review, preference data, error analysis and safety-focused testing.
Evaluate outputs through a clinical lens
Clinical language-model evaluation can combine structured rubrics, expert judgment and scenario-based testing.
Clinical Accuracy
Assess factual and clinical correctness against the task and available context.
Hallucination Review
Identify unsupported, fabricated or misleading clinical statements.
Safety
Evaluate responses for potentially harmful or inappropriate recommendations.
Usefulness
Assess whether outputs are relevant, understandable and useful for the intended user or workflow.
Preference Data
Collect structured expert preferences between candidate model responses.
Error Taxonomy
Categorize recurring model failures to support targeted improvement.
Build your healthcare AI data workflow with SCILabel
Talk to our team about your healthcare data, annotation or AI evaluation requirements.