Medical image annotation for radiology and pathology AI teams

Clinician reviewers on the modality, documented quality gates and EEA processing, for models in development, evaluation and research.

Per task Quality metric
In contract Delivery gate
Clinical Reviewers
Scope a medical imaging project Book a scoping call
Fine-grained geometry
GDPR-aligned handling
Clinical expertise
Planned scale
Brain MRI
Pathology
3D anatomy

Medical imagery labelled for clinical AI

Clinical reviewers on the modality they practise

Medical image annotation labels scans and slides so that models can learn to detect disease, assist in diagnosis and support clinicians.

An example task: marking, on hundreds of lung X-rays, exactly where pneumonia is present, so a model learns to find it in X-rays it has not seen.

1

Raw medical imagery

X-rays, CT scans, MRIs, ultrasounds or pathology slides

2

Clinical annotation

Task-appropriate clinical experts mark the agreed structures and findings

3

Quality review

Two passes and adjudication, reported against the task-specific acceptance plan

4

Model training

Annotated datasets train healthcare AI models

Per task
Quality metrics
matched to the imaging task
Clinical
Review model
with defined qualifications and roles
Recorded
Review decisions
from guideline to adjudication

Clinical reviewers, defined gates, EEA handling

Medical imaging annotation needs the right clinical reviewer, a written acceptance plan, and documented handling of the images.

Clinical reviewers

  • Specialised medical training
  • Clinical expertise on the modality

Two passes and adjudication

  • Second review pass
  • Expert consensus on disagreements

DICOM and 3D volumes

  • DICOM format support
  • 3D volumetric handling

Access control and data protection

  • GDPR-aligned controls
  • Least-privilege access

Protocols per project

  • Workflow set for the task
  • Guidelines updated as the task evolves

Planned capacity

  • Capacity planning
  • Performance reporting
Per task Quality metric
Documented Data governance
Clinical Reviewers

In healthcare AI, quality is a task-specific responsibility. The metric, reference set, reviewer roles, uncertainty handling, and acceptance gate must all be explicit.

Where medical image annotation is used

Medical imaging annotation for model development, evaluation and research across radiology, pathology, surgery and ophthalmology.

For studies read by credential-verified clinical readers, with two-reader agreement, adjudication and a per-study QA record, see medical imaging annotation by clinical readers.

Radiology
X-ray, CT and MRI

Diagnostic radiology models

  • Pneumonia detection
  • Breast lesion marking
  • Brain tumour mapping
Pathology
Pathology slides

Pathology and cancer research

  • Colon biopsy annotation
  • Cancer cell differentiation
  • Treatment effect measurement
Surgery
3D volumes

Surgical planning

  • Liver tumour segmentation
  • Blood vessel mapping
  • Joint implant planning
Ophthalmology
Fundus and OCT

Ophthalmology and digital health

  • Fundus photograph labelling
  • OCT scan analysis
  • Retinopathy classification
Multimodal
Imaging with paired data

Multimodal healthcare models

  • Brain MRI with health records
  • Ultrasound video with ECG
  • Genomic data paired with imagery
  • Longitudinal data for disease progression

What the engagement includes

High-stakes imaging work needs explicit qualifications, reference sets, uncertainty handling, adjudication, governance, and acceptance reporting.

Clinical expertise

Task-specific credentialsNamed review roles

Controlled data handling

GDPR-aligned controlsEEA options

Clinical quality plan

Metric set per taskReference-set evaluation

Planned scale

Pilot to productionContract delivery gates

Medical QA

CalibrationReview and adjudication

Accountable delivery

Named project leadAcceptance reporting

YPAI designs the annotation and QA plan around the clinical task, your requirements and the agreed acceptance criteria.

Scope a medical imaging pilot

Every label traceable from guideline to adjudication

From pathology boundaries to findings in chest X-rays, define the task, imaging format, expert roles, de-identification requirements, quality metrics, and evidence package for a scoped pilot.

DPA availableDICOM support

GDPR Article 9 controls • De-identification protocols • Documented human QA

Modalities (optional)

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YPAI processes personal data under the EU General Data Protection Regulation (GDPR) and applies its principles to every project, wherever it runs.

Privacy by design

Designed with privacy in mind from inception. Minimum data principle applied.

Lawful basis and consent

Clear legal basis established. Transparent consent gathering.

Data subject rights

Access and portability. Rectification and erasure. Restriction and objection.

Secure EU storage

EU-based secure environments. Standard Contractual Clauses for transfers.

Vendor and sub-processor management

Strict vendor register. Compliance review of all sub-processors.

Continuous governance

Regular internal audits. Team privacy training.

Data Protection Officer

Submit a data request

Response time

All data requests processed within 30 days

Compliance standards

GDPR, CCPA, and global privacy regulations