Healthcare AI Data Annotation Services

Healthcare AI

Medical-grade data for medical-grade models

Synnth pairs clinical annotators with HIPAA-ready infrastructure to label the imaging, text, and speech data behind diagnostic AI, clinical NLP, and patient-facing healthcare systems.

Overview

Healthcare AI carries a different bar than most machine learning applications — mislabeled data doesn’t just hurt model accuracy, it risks patient outcomes. Synnth’s healthcare annotation work is done by a network of medical professionals, not generalist crowdworkers, and every project runs through HIPAA-ready handling and de-identification protocols from intake to delivery.

What we do

Data collection & annotation across every AI modality

From sourcing raw data to delivering production-ready labeled datasets — Synnth covers the full pipeline across the four core data types that power modern AI.

Data collection

De-identified clinical note and EHR text sourcing
Medical imaging dataset assembly (radiology, dermatology, pathology)
Clinical speech and dictation recording
Patient-consented multilingual health data collection
Synthetic patient data generation for rare-condition augmentation

Annotation

Radiology report labeling and structured findings extraction
Medical image segmentation (CT, MRI, X-ray, ultrasound, histopathology)
Lesion, tumor, and anomaly bounding and contouring
Clinical NLP: symptom extraction, medication and dosage tagging, ICD/SNOMED coding support
Clinical speech transcription and medical terminology annotation
Clinical speech transcription and medical terminology annotation

Why Synnth for healthcare AI

Every Synnth project follows the same structured pipeline, regardless of data type, language, or volume. That consistency is not bureaucratic — it is the mechanism through which we maintain quality at scale.

Credentialed medical annotators. Our healthcare pool includes licensed clinicians, radiologists, and nurses matched to the specific modality and specialty of your dataset — not generalists reading a labeling guide for the first time.

HIPAA-ready by design. Access-controlled environments, encrypted storage, PHI redaction workflows, and NDAs on every engagement, with processes aligned to HIPAA and ISO 27001 standards.

Clinical terminology accuracy. Annotators are trained on medical coding standards (ICD-10, SNOMED CT) and specialty-specific vocabulary, reducing the rework that generic annotation teams create for clinical NLP models.

Multi-reviewer sign-off on diagnostic-adjacent labels, with inter-annotator agreement thresholds set higher for datasets feeding diagnostic or triage models.

Delivery formats

2,000+ expert annotators, matched to your domain

DICOM-compatible exports, COCO/JSON for imaging, FHIR-aligned structured text, or your institution’s existing schema.

Ready to build AI on data you can trust?

Whether you need a small pilot batch or an ongoing production annotation pipeline — tell us what you’re building and we’ll tell you exactly how we can help.

FAQs

Common questions

Everything you need to know before starting a AI Data collection or annotation project with Synnth.

💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.

Are your annotators medically qualified?

Yes. Healthcare projects are staffed with credentialed medical professionals matched to the relevant specialty — radiology, pathology, clinical nursing, and more.

How do you handle patient data privacy?

All data is processed under strict access controls, encrypted at rest and in transit, and handled per HIPAA-ready protocols, including PHI de-identification before annotation begins.

Can you support rare-condition or low-volume datasets?

Yes, through targeted sourcing campaigns and synthetic data augmentation to build sufficient training volume for rare presentations.

Get started

Start your AI Data collection or annotation project today

Tell us your use case, action taxonomy, environment, and volume. Our team responds within one business day with a scoping plan and no-obligation quote.