Healthcare AI annotation with clinical caution built in
Medical imaging labels, clinical text structure, and sensitive-data workflows designed for health AI leaders — not consumer-content freelancers.
Healthcare data is not a marketplace commodity
Clinical language, imaging ontologies, and privacy constraints punish vendors who treat every dataset like product photos.
- Inconsistent lesion / region boundaries across annotators
- Clinical terminology mis-tagged by non-domain reviewers
- Unclear PHI handling and access expectations
- Slow procurement friction with offshore-only vendors
Imaging precision
Region labels calibrated to your radiology or pathology guideline pack.
Clinical text structure
Entity, section, and classification work grounded in your schema — not generic NLP templates.
HIPAA-aware process
Least privilege, NDAs, DPA support, and customer-controlled environments when required.
Labeling for medical AI products
We combine trained annotators, multi-layer QA, and procurement-friendly documentation so your pilot can clear diligence and still move fast.
Medical image annotation
Boxes, polygons, and regions for imaging workflows; attribute schemas mapped to your clinical guideline version.
Clinical text labeling
Classification, extraction, and structured review for notes, reports, and medical documents.
HIPAA-aware handling
Access control, NDA/DPA pathways, and customer-defined sensitive-data rules. Not a claim of blanket certification.
BYOP in your tenancy
Keep PHI/PII and audit controls in tools you already approve; we supply the trained workforce and QA ops.
Gold & review QA
Consensus sampling, expert review lanes where needed, and clear rework thresholds.
Guideline change control
When clinical definitions evolve, we update training and stop the line until consistency recovers.
Security language procurement teams expect
US-registered commercial entity, controlled workforce onboarding, and documented security posture for diligence questionnaires.
DPA & NDA ready
Templates and custom handling clauses available before sensitive pilots begin.
Least-privilege workflows
Role-based assignment and no shared informal channels for regulated content.
Public security brief
Read the summary at mlatho.com/security and request the whitepaper/DPA.
Trained via Mlatho Learn
Workers complete structured training and Lab scoring before high-stakes production work.
A careful pilot path
Define handling
Data class, tooling, NDA/DPA, and environment constraints
Calibrate labels
Guideline training on de-identified or approved samples
Pilot with QA
Measured accuracy, disagreement review, and report
Scale carefully
Expand volume only after gates are stable
Questions from healthcare AI teams
Ready for a careful healthcare data pilot?
Share your modality, schema, and handling constraints. We will propose a scoped pilot that diligence teams can accept.
Request a Demo