DFKI

Expertise · Synthetic Medical Data

Synthetic Medical Data

Generate realistic medical datasets for safer model development and GDPR-compliant collaboration.

Research Focus

Scientific excellence applied to real-world healthcare.

DFKI researches generative methods — diffusion models, GANs and simulation-based approaches — to produce high-fidelity synthetic medical data. Synthetic datasets support AI development where real data is scarce, sensitive or difficult to share across institutions.

Methods and capabilities

  • Diffusion and generative models for imaging
  • Tabular and time-series data synthesis
  • Simulation-based data generation
  • Privacy and utility evaluation
  • Rare-condition and edge-case augmentation
  • Federated and cross-institutional pipelines

Application areas

  • Model training with limited data
  • GDPR-compliant collaboration
  • Rare disease and edge-case research
  • Benchmark dataset creation
  • Regulatory and validation studies

Engagements typically start with a feasibility study or joint research project — evaluating data, methods and impact before wider deployment.

Related Expertise & Services

Where this research connects.

Complex healthcare challenges rarely map to a single method. Our expertise areas combine to support broader clinical, research and product initiatives.

Privacy-Preserving AI

Differential privacy and anonymization for secure medical AI.

Learn more

Medical Image Analysis

Segmentation, classification and multimodal analysis for imaging.

Learn more

AI Pilot Program

Structured pilots from validated idea to real-world evidence.

Learn more

Explore this research expertise with DFKI.

From feasibility studies to long-term research collaboration — start with a structured conversation about your challenge.

Book a Goal AssessmentSee AI Strategy Workshop