Imagine a giant, secure digital library that collects health records, wearable device data, and medical scans from all over Europe. Instead of this information being locked in separate folders, it's all organized so AI can spot lung cancer patterns earlier. It's like giving doctors a high-tech crystal ball to predict how a patient will recover based on thousands of similar cases.
Medical data is currently fragmented across different systems, making it nearly impossible for regulators and doctors to get a full picture of a patient's journey. This slows down drug approvals and prevents early cancer detection.
A scalable EU-wide health data platform that integrates EHRs, CT images, and wearable data. It includes AI tools for risk profiling and a patient-controlled data access system.
If you are a drug developer dealing with slow regulatory approvals for new therapies — this project developed a platform for real-world evidence that helps regulatory and HTA bodies make faster decisions. This reduces the time to get treatments to market by using integrated heterogeneous data.
If you are a wearable tech manufacturer dealing with a lack of clinical validation for your sensors — this project developed a way to integrate digital biomarkers and connected devices into clinical care. This allows your device data to be used for shared decision-making between patients and professionals.
If you are a hospital network dealing with overcrowded wards and high readmission rates — this project developed remote patient monitoring tools and AI risk profiling. This enables earlier patient discharge and reduces the burden on healthcare staff.
Based on available project data, no specific pricing or cost details are provided as the project is EU-funded.
Yes, the objective states the platform is directly scalable by connecting additional systems, data sources, and services, though the primary use cases are lung cancer.
Based on available project data, specific licensing terms are not listed, but the project focuses on creating an open platform for multi-modal health data.
The platform prioritizes adherence to legal requirements, specifically the European Health Data Space (EHDS), and includes a solution for patients to control who accesses their data.
It uses a remote access approach to connect multiple public and private data sources, incorporating interoperable tools and AI/ML for analysis.
The consortium is heavily industry-driven, with 19 industry partners representing 49% of the 39 total members. This high industrial ratio, combined with a presence in 12 countries, suggests a strong focus on commercial viability and cross-border regulatory alignment rather than purely academic research.
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