Imagine if hospitals across Europe could share medical secrets to find patterns in heart disease without actually swapping private patient files. This system acts like a secure bridge that lets AI learn from different data sources while keeping the actual records locked away. It helps doctors spot heart problems earlier and pick the best treatment for each person based on a massive pool of shared knowledge.
Healthcare systems struggle with fragmented, siloed cardiovascular data that is difficult to share due to privacy laws. This prevents the use of AI to predict heart disease early, leading to high management costs and millions of preventable deaths.
A federated Platform-as-a-Service (PaaS) for secure data sharing and a suite of AI-driven precision medicine tools for CVD diagnosis and risk stratification.
If you are a software developer dealing with fragmented medical data across borders — this project developed a federated platform-as-a-service that allows for secure sharing and automated curation of data. This enables the creation of precision medicine tools for better diagnosis and risk stratification.
If you are a pharma company dealing with the need for diverse genomic and clinical datasets for heart medication — this project developed pipelines that integrate retrospective datasets and biobanks from 7 countries. This accelerates the identification of targets for cardiovascular interventions.
If you are a hospital network dealing with high costs of long-term patient management — this project developed AI-driven tools for early risk identification. This helps reduce the overall cost of patient management by shifting toward preventive care.
Based on available project data, the specific commercial pricing is not mentioned, but the project is developing a platform-as-a-service (PaaS) model and conducting a cost-effectiveness analysis to promote adoption.
Yes, the project is specifically designed as a European-wide federated platform involving 11 countries and validating tools in 5 countries to ensure wide-scale applicability.
Based on available project data, specific licensing terms are not provided, but the project focuses on creating a legally compliant infrastructure for secure data sharing.
Yes, the platform is built using a privacy-by-design approach to ensure seamless and legally compliant integration and secure sharing of data.
The project timeline runs from 2024-01-01 to 2027-12-31, indicating a 48-month development and validation cycle.
The system uses an extended OMOP Common Data Model to integrate clinical, genomic, and imaging data from heterogeneous sources.
The consortium is well-balanced for commercialization, featuring 19 partners across 11 countries. With a 32% industry ratio (6 companies, including 5 SMEs), there is a strong link between the 7 universities and 4 research institutes and the actual market. The leadership by an SME (Software Imagination and Vision SRL) suggests a focus on deliverable software products rather than purely academic research.
Contact Software Imagination and Vision SRL in Romania
Contact us to explore licensing opportunities for the federated AI pipelines.
