SCILabel | Healthcare AI Data, Done Right

Data Annotation & Labeling

Healthcare AI Data Annotation | SCILabel

What is Healthcare AI Data Annotation?

Raw healthcare data — a DICOM scan, a clinical note, a doctor–patient conversation — cannot train an AI model as‑is. It must be labeled: structures identified, findings named, codes assigned, relationships mapped. This is annotation. In healthcare, it must be done by people who understand the clinical content — not generic crowd workers. SCILabel's annotation service delivers clinical‑grade labeled datasets through a certified workforce and a multi‑tier quality assurance pipeline.

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Healthcare AI data annotation illustration

The Annotation Lifecycle | SCILabel

The Annotation Lifecycle

Step	Action	Quality Gate
1	Client uploads data and submits project specifications (annotation guidelines, format, deadline) through the SCILabel Client Portal	NDA and DPA signed; data encrypted at upload
2	Project Manager reviews specifications; QA proposes pricing and timeline; client approves	Pricing based on volume, complexity, specialty, and turnaround
3	Task Engine routes tasks to certified taskers matched to the required specialist track	Only taskers with the correct track qualification receive tasks
4	Taskers annotate in the SCILabel Annotation Workspace using appropriate tools (image, NLP, audio, genomic)	Taskers work only within their approved clinical tracks
5	Completed tasks enter the QA pipeline: Tier 1 peer review, Tier 2 QA reviewer, Tier 3 PM spot-check (5–10%)	Gold-standard benchmark tasks embedded passively to measure accuracy
6	QA approves batch or returns with line-by-line feedback for rework; IAA scores calculated	Tasks not meeting quality threshold are reworked before re-submission
7	Approved, certified dataset delivered to client with completion report, IAA scores, and QA certification	Client downloads from secure data room with full audit trail

Annotation Capabilities | SCILabel (Carousel)

Annotation Capabilities by Type

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Medical Image Annotation

Bounding box annotation (2D and 3D) on DICOM and standard image formats
Polygon, freehand, and spline segmentation for irregular lesions and structures
Semantic and instance segmentation for organ and tissue delineation
Keypoint and landmark annotation for anatomical reference models
Multi-frame DICOM navigation with window/level adjustment
Classification labels: finding type, severity, laterality, certainty

Clinical NLP Annotation

Named entity recognition: diseases, symptoms, medications, procedures, anatomy, lab values
Relation extraction: medication–indication, symptom–diagnosis, procedure–outcome
Assertion classification: present/absent/possible/historical/family history/hypothetical
Code assignment: ICD-10-CM, ICD-10-PCS, SNOMED CT, LOINC, RxNorm, MedDRA, CPT, HCPCS
Document and sentence classification: clinical specialty, document type, care setting

Audio & Speech Annotation

Verbatim and clean-read transcription with speaker diarisation
Clinical entity tagging in transcripts: symptoms, diagnoses, medications, procedures
SOAP note structure annotation from conversation transcripts
Dialogue act and intent labeling for conversational AI

RLHF & AI Response Annotation

Side‑by‑side AI response comparison and preference ranking
Multi‑criterion scoring: Accuracy, Relevance, Safety, Clarity, Completeness
Free‑text rationale collection for reward model training
Binary safe/unsafe labeling for safety classifiers

Genomic & Biomedical Data Annotation

Genomic variant pathogenicity classification (ACMG 5‑tier)
Biomarker relevance labeling for oncology and pharmacogenomics models
Gene/protein entity tagging in biomedical literature
Adverse event term normalisation (MedDRA hierarchy)