RealAI's flagship healthcare deployment with the European Health Network pairs Hominis vision models with clinical workflows. It identifies patients at risk of chronic disease months before traditional methods, while meeting full GDPR and clinical-governance requirements.
The challenge
Strict GDPR and medical-privacy compliance. Highly imbalanced datasets, where positive cases are a small fraction of the population. Clinical-grade accuracy for medical decision support. And, hardest of all, physician trust and adoption inside the clinical workflows that already exist.
The approach
A federated learning framework trains models across multiple hospitals without patient data ever leaving its source system. Privacy compliance is built in by design, not bolted on by exception. On top of it, a novel attention-based architecture produces interpretable explanations for every prediction, exposing the specific risk factors that drive each assessment.
That transparency (not raw accuracy) is what won clinical adoption and regulatory approval.
The results
In clinical trials across five hospitals, the platform reached 89% sensitivity and 92% specificity for early chronic-disease detection: accurate enough to act on, explainable enough to trust.
- 4.2 mo
- Earlier detection
- 35%
- Better patient outcomes
- 20%
- Lower healthcare costs
Time-to-diagnosis fell by 4.2 months on average, patient outcomes improved 35% in the intervention group, and healthcare costs dropped 20% through prevention-focused care. The platform now anchors RealAI's CytoDeep module on the Hominis stack.