Clinical Annotation Across Complex Healthcare Modalities
SCILabel combines trained healthcare data annotators, clinical reviewers and structured QA workflows to create labels and ground truth for healthcare AI.
Annotation workflows matched to the modality
Different medical data types require different tools, ontologies, expertise and quality controls.
Medical Imaging
Classification, localization, bounding boxes, segmentation, measurements and structured metadata tasks.
Digital Pathology
Whole-slide classification, tissue regions, cells, nuclei and pathology-specific labeling workflows.
Physiological Signals
ECG, EEG and other signal annotation including rhythms, events, intervals and quality review.
Clinical Text
Entity annotation, classification, structured extraction and clinical NLP workflows.
Medical Audio
Transcription, speaker or interaction structure and project-specific clinical labeling.
Medical Video
Temporal events, objects, procedures, devices and frame- or segment-level annotations.
From guideline to reviewed annotation
Projects can combine contributor onboarding, project-specific instructions, annotation, correction, reviewer QA and final validation.
Guidelines
Define labels, examples, exclusions, edge cases and escalation rules.
Annotation
Assign structured tasks to appropriately trained contributors.
Review & Rework
Route selected work through reviewer feedback and targeted correction.
QA
Apply project-specific validation before final acceptance.
Build your healthcare AI data workflow with SCILabel
Talk to our team about your healthcare data, annotation or AI evaluation requirements.