Imagine a crystal ball for health that tells parents and doctors if a child is likely to develop heart or metabolic problems years before they happen. It uses smart computer programs to spot patterns in a child's habits and health data. This helps families make small lifestyle changes now to avoid serious illnesses later in life.
Health professionals and parents cannot reliably identify children at high risk for chronic diseases until it is too late. This leads to missed opportunities for early intervention and contributes to a massive global healthcare cost burden.
AI-based risk-prediction models for cardiovascular and metabolic diseases and two user applications (one for professionals, one for citizens) that suggest specific behavior changes.
If you are a health app developer dealing with low user retention in wellness apps — this project developed AI risk-prediction models that provide personalized behavior change recommendations. This allows your app to offer high-value, targeted health interventions for users aged 5–19.
If you are a pediatric clinic dealing with the inability to identify high-risk children for lifestyle interventions — this project developed a professional application that breaks down risks by factor. This enables your staff to provide timely and accurately targeted care to prevent chronic NCDs.
If you are an insurance provider dealing with the rising costs of chronic NCDs — this project developed tools to predict long-term health risks in youth. By promoting these tools, you can reduce future claims related to cardiovascular and metabolic diseases.
Based on available project data, no specific pricing or cost per license is mentioned; however, the project is developing an exploitation and sustainability plan to ensure future financial footing.
Yes, the project is validating the tools through a proof-of-concept study in five different real-world healthcare scenarios across four countries to ensure widespread adoption.
Based on available project data, specific licensing terms are not listed, but the project includes a dedicated exploitation plan to manage the solution's future.
Yes, the project employs federated learning specifically to safeguard data privacy during the training of AI models.
The project period runs from 2023-05-01 to 2027-04-30, suggesting the final validated tools will be ready toward the end of this window.
The consortium is well-balanced for a translation project, consisting of 14 partners across 9 countries. With a 29% industry ratio (4 companies, including 3 SMEs), there is a strong bridge between the 8 universities and 2 research centers and the commercial market, ensuring the AI tools are designed for practical use rather than just academic publication.
Contact Institut Jozef Stefan in Slovenia for partnership opportunities.
Contact us to explore licensing the AI risk-prediction models for your health product.
