Introducing DataLbl
Introducing DATALBL: GDPR-safe synthetic healthcare datasets, clinical transcripts, and imaging metadata for AI training and evaluation.
Posted by
Graeme Rycyk
Synthetic data for regulated AI
DATALBL provides GDPR-safe synthetic healthcare datasets that retain statistical utility without exposing PHI. We deliver structured health records, clinical transcripts, and imaging metadata that teams can use to train and evaluate AI systems in regulated environments.
What we offer
- Structured Health Records — tabular data with ICD-10 outcomes, procedures, labs, and demographics.
- Doctor–Patient Transcripts — synthetic clinical dialogues for ASR/NLP model training.
- Imaging Metadata — study-level metadata suitable for triage and workflow models.
Every dataset ships with an Evidence Pack: schema.yaml, data-dictionary.csv, QA and governance notes, leakage proxies, and optional FHIR‑lite exports.
Governance and compliance
Our generation pipeline includes PII scanning, k-anonymity style analysis, and documentation aligned to GDPR principles and the EU AI Act risk framework. We publish transparency artifacts so your compliance and security teams can review lineage and safeguards.
Evaluating DATALBL data
Use our samples to quickly test label prevalence, cohort balance, and model baselines. Compare our leakage proxy and bias summaries against your acceptance criteria before piloting full datasets.
Get started
Browse the Dataset Catalog or contact us for a guided pilot. We can tailor distributions, vocabularies, and temporal coverage to your use case.