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Industry — Healthcare & Life Sciences

Medical AI Data
You Can Trust

Clinical AI has a higher quality bar than any other domain. Errors in medical AI can cause harm. Our healthcare datasets are annotated by licensed clinicians, de-identified to HIPAA standards, and sourced from real clinical environments.

Datasets

What We Offer for Healthcare & Life Sciences

Radiology
Multi-Modality Radiology
X-ray, CT, MRI annotated by licensed radiologists. 12 pathology categories. HIPAA-compliant.
📦 4.2M scansHIPAA
  • Board-certified radiologist annotations
  • Structured DICOM SR reports
  • Multi-modality: X-ray, CT, MRI, PET
Pathology
Digital Pathology Slides
Whole slide images annotated by board-certified pathologists. Oncology, infectious disease, inflammatory conditions.
📦 850K slides🔬 Expert
  • Cell-level segmentation annotations
  • Tumor grade and stage labels
  • Pathologist confidence scores
Clinical NLP
De-identified EHR Notes
Clinical notes and discharge summaries with NER annotations. De-identified beyond HIPAA Safe Harbor.
📦 28M notes🔒 De-identified
  • Disease, drug, procedure NER
  • Negation and uncertainty flags
  • ICD-10 and SNOMED codes

Compliance

Healthcare & Life Sciences Compliance

  • De-identification exceeds HIPAA Safe Harbor — 18 identifiers removed plus secondary risk assessment
  • All clinical partners operate under executed Business Associate Agreements (BAAs)
  • IRB approval documentation available for academic and research use cases
  • Patient consent records maintained and verifiable per regulatory requirement
  • Data encrypted at rest (AES-256) and in transit (TLS 1.3)
  • Full data lineage from patient encounter to dataset delivery, auditable on request
GDPR
Compliant
HIPAA
Certified
CCPA
Compliant
PII-Free
Every Dataset
Encrypted
End-to-End
Audit
Ready
Full Docs

Build Clinical AI That Actually Works

Talk to our medical data team about your clinical use case and compliance requirements.

Talk to Our Team →Browse Datasets
Visual Sample

What Our Medical Annotations Look Like

Every medical dataset delivered by DATALENT includes structured diagnostic reports alongside pixel-level annotations. Our radiologists use standardized DICOM SR templates so the output integrates directly with clinical AI pipelines without reformatting.

Annotation confidence scores, secondary review flags, and pathologist agreement metrics are included per sample — so you know exactly how reliable each label is before it enters your training loop.

Request Medical Sample →
DATALENT medical AI annotation interface showing radiology scan with AI detection boxes, confidence scores, and radiologist annotations
Example: Chest X-ray with AI-assisted annotation and radiologist verification
FAQ

Healthcare Data Questions

What de-identification standard do you use for medical data?
We exceed HIPAA Safe Harbor requirements. Beyond the 18 standard identifiers, we run secondary risk assessments and apply quasi-identifier suppression where necessary. Every de-identified dataset ships with a documented de-identification protocol and a compliance certificate. All clinical partners operate under executed Business Associate Agreements.
Are your medical annotators licensed clinicians?
Yes, without exception. Radiology datasets are annotated by board-certified radiologists. Pathology slides by certified pathologists. Clinical NLP by trained medical coders and physicians. We do not use crowd-sourced annotators for any medical data. All annotators go through a qualification process before being assigned to annotation tasks, and inter-annotator agreement is tracked on every project.
Can we get data from a specific clinical population or condition?
Yes. Through our clinical partner network, we can source data targeted to specific conditions, demographics, imaging modalities, or geographic populations. Rare condition oversampling is a common request — we can design capture programs specifically to build datasets with appropriate representation of uncommon presentations. Contact our medical data team to discuss your specific requirements.