Imagine if hospitals could share the 'wisdom' of their patient data without actually sharing the private records themselves. This project builds a secure network where AI learns from many different clinics to predict stroke risks and treatments more accurately. It is like a global brain for doctors that helps them give every patient a tailor-made recovery plan.
Stroke care is often fragmented, with inconsistent data and protocols across different hospitals. This leads to slower diagnosis and less personalized treatment, which negatively impacts patient recovery rates.
A federated data platform (U-platform) and a Federated Learning infrastructure (FL-platform) for training AI models without sharing raw patient data.
If you are a software developer dealing with strict data privacy laws—this project developed a Federated Learning platform that allows AI training across 7 top-tier clinical centres without moving sensitive data. This enables the creation of validated, trustworthy AI models for risk prediction.
If you are a hospital manager dealing with inconsistent patient care standards—this project developed standardized stroke protocols and digital monitoring tools. This improves professional workflows and patient outcomes across the entire care pathway.
If you are a tech provider dealing with poor patient engagement after discharge—this project developed digital technologies for real-time monitoring and data visualization. This helps bridge the communication gap between patients and doctors during rehabilitation.
Based on available project data, no specific pricing or cost information is provided.
Yes, the project uses a multi-country federated approach involving 34 partners across 12 countries, designed to set a leading model for stroke management by 2030.
Based on available project data, specific licensing terms are not listed, but the project includes a roadmap for long-term sustainability and exploitation.
The project has a specific objective (OB5) to develop a roadmap for regulatory compliance and future certification for the AI platform and management cases.
It uses Common Data Models (CMDs) implemented in 7 clinical centres to harmonize data and standardize procedures across the pre-, in-, and post-hospital pathway.
The consortium is heavily industry-weighted with a 59% industry ratio (20 companies), including 6 SMEs. This strong commercial presence, combined with 12 countries and 34 total partners, suggests a high focus on market viability and practical implementation rather than purely academic research.
Contact Fundacio Hospital Universitari Vall d'Hebron - Institut de Recerca
Contact us to explore licensing opportunities for the U-platform AI models.
