EdenBuilding Generative AI for Personalized Medicine.
Synthetic clinical data, validated for research.
Eden generates patient cohorts that keep the statistical and clinical properties of real data, so research teams can study, share and validate without exposing patient records.
A spin-off of IRCCS Istituto Clinico Humanitas.
Clinical Areas:
Same Statistics. Different Patients.
The synthetic cohort reproduces the shape of the real data, its distributions and the relationships between variables, but no synthetic patient corresponds to a real one.
Illustrative visualization. Each dot is one patient record; in the synthetic view, rings mark where the real records sit.
Variable A, distribution:
Variable B, distribution:
- Distributions preserved: each variable keeps the shape of the original.
- Relationships preserved: correlations between variables carry over.
- No one-to-one link: no synthetic record maps back to a real patient.
Research-Grade Data, Without Exposing Patients.
Synthetic data is produced by AI models that reproduce the statistical and biological characteristics of a real dataset, while containing nothing that can be traced back to an individual patient.
Synthetic Cohorts:
Generate patient populations that keep the properties of the source data, with no record traceable to a real person.
Synthetic Control Arms:
Build synthetic comparators that complement control groups in clinical studies and ease the burden of enrollment.
Rare-Disease Research:
Augment small cohorts where real data is scarce, as in rare hematological diseases, so that analyses become possible.
Digital Twins:
Model patient trajectories over time and explore clinical scenarios before they reach the bedside.
From Source Data to Validated Synthetic Data.
Three steps take a study from a curated clinical dataset to a synthetic cohort ready for analysis.
- 1Connect:
Start from a curated clinical dataset and define the population, variables and endpoints the study needs.
- 2Generate:
Generative models learn the structure of the source data and produce a synthetic cohort at the scale the study requires.
- 3Validate:
Each dataset is assessed for statistical and clinical fidelity with SAFE, the Synthetic vAlidation FramEwork by Train.
Born Inside a Research Hospital.
Train is a spin-off of IRCCS Istituto Clinico Humanitas that builds generative AI for healthcare: synthetic clinical data, digital twins and clinical voice AI.
Its work grows out of an ecosystem where clinicians, researchers, engineers and data scientists work side by side, so every model is shaped by clinical questions first.
Clinical Roots:
Spin-off of IRCCS Istituto Clinico Humanitas, working alongside the Humanitas AI Center.
Validation:
SAFE measures the statistical and clinical fidelity of every synthetic dataset.
European Research:
Experience across EU programs such as Synthema and GenoMed4ALL.
Book a Discovery Call.
Tell the Train team about your study or data challenge. A specialist will reply from info@train-ai.eu to arrange a call at a time that suits you.
- A guided walkthrough of the Eden platform
- A first look at your use case and the data behind it
- Clear next steps on access, generation and validation
Prefer to write directly? info@train-ai.eu