PATH proposes a federated, privacy-preserving health data space that integrates secure data exchange, AI, and digitalization to transform how cancer is detected, diagnosed, treated, and managed long-term. The strategy builds on three interlinked innovation pillars.
i) A secure health data space delivering interoperability, data sovereignty, and bias mitigation through representative datasets, FAIR-aware algorithms, continuous bias audits, and transparency mechanisms, ensuring AI models reflect diverse populations and prevent healthcare disparities.
ii) Federated learning and Trusted Execution Environments (TEEs) for continuous model optimization, refining AI tools on real-world clinical data without compromising patient privacy, ensuring explainable, bias-aware, and generalizable decision support across heterogeneous healthcare settings.
iii) An AI-powered dashboard within the federated infrastructure, integrating AI tools pre-trained in previous projects and connecting hospitals, clinicians, patients, and developers in a community enabling large-scale validation, metadata-driven classification, and FAIR-based market expansion.
PATH will serve as a platform for boosting innovation in the assessment of integrated cancer care solutions, providing a data-rich environment to evaluate diagnostics, decision-support systems, and digital health tools, strengthening health technology assessment, regulatory decision-making, and reimbursement strategies. The PATH framework will be unfolded in three phases: development of the data space and core AI toolset; pilot studies across EU hospitals and patient organizations evaluating prognosis, survivability, re-hospitalization risk, and healthcare costs; and consolidation through sustainable business models, digital literacy training, and cross-sector collaboration. Delivered by a 24-partner Public–Private Partnership, PATH aims to establish a safe, ethical, and sustainable paradigm for AI-driven cancer care aligned with Europe’s Beating Cancer Plan and the EHDS.