Imagine if all the scattered medical notes and test results from different hospitals could talk to each other. This project builds a secure digital bridge to connect that data and uses smart AI to spot patterns. It helps doctors pick the right treatment for patients with autoimmune diseases much faster, like having a GPS for a patient's health journey.
Diagnosis of chronic immune diseases is often delayed and treatments are not personalized, leading to inefficient care. Medical data is currently too fragmented across borders to be useful for large-scale AI training.
The project is building secure, explainable AI predictive models and a federated data-sharing infrastructure for risk stratification and outcome prediction.
If you are a software developer dealing with fragmented medical datasets — this project developed secure, explainable AI-driven predictive models that enable earlier diagnosis and better therapy selection for 10% of the population affected by CIMDs.
If you are a pharma company dealing with imprecise patient stratification for clinical trials — this project developed computational risk stratification tools that help identify the right patient profiles for specific therapies.
If you are a clinic manager dealing with high costs from over-treatment or under-treatment — this project developed a blueprint for data-driven monitoring that reduces healthcare costs linked to avoidable disability.
Based on available project data, no specific pricing or cost structures are mentioned as the project is currently in the research and development phase.
Yes, the project creates a scalable blueprint for managing other chronic diseases beyond the initial immune-mediated use cases.
Based on available project data, specific licensing terms are not provided, but the project focuses on GDPR-compliant frameworks for data sharing.
The project uses federated access and GDPR-compliant frameworks to ensure secure, trustworthy data sharing across borders.
The project focuses on harmonizing health data across borders and addressing technical barriers to data integration to ensure accessibility.
The consortium is well-balanced for commercialization, featuring a 27% industry ratio with 4 industrial partners, including 2 SMEs. The heavy academic presence (8 universities) ensures deep clinical validity, while the inclusion of a Patient Organisation ensures the end-user needs are met, reducing market entry risk.
Contact Karolinska Institutet regarding the WISDOM project coordination.
Contact us to explore licensing opportunities for the predictive AI models developed by WISDOM.
