Federated Learning and EHDS as Policy Data Infrastructure for Territorial Health Planning in Ageing Europe

Demographic decline, population ageing and the progressive desertification of healthcare services in many European territories pose major challenges for territorial cohesion and equitable access to care. The European Health Data Space (EHDS) establishes a new regulatory framework for the secondary use of health data across Member States, opening opportunities for advanced analytics under strict data protection safeguards. This paper argues that federated learning (FL), when embedded within th...

FL-EHDS: A Privacy-Preserving Multimodal Federated Learning Framework for the European Health Data Space

The European Health Data Space (EHDS), established by Regulation (EU) 2025/327, mandates cross-border health data analytics while preserving citizen privacy. We present FL-EHDS, a three-layer compliance framework integrating EHDS governance (Health Data Access Bodies, data permits, Article 71 opt-out) with federated learning orchestration (17 algorithms including ICML/ICLR 2024±2025 advances, differential privacy, secure aggregation) and data holder components (FHIR R4 preprocessing). Multimo...

Augmented Functional Random Forests per la classificazione interpretabile di traiettorie biomediche funzionali

Clinical Reliability of Large Language Models in Complex Haematology: A Multidimensional Evaluation in Hemophilia–Oncology

Background: The co-existence of hemophilia and cancer presents one of the most complex clinical scenarios, demanding individualised therapeutic planning to balance oncologic efficacy and hemostatic safety. This study evaluated the ability of two Large Language Models (LLMs)—ChatGPT (GPT-4) and Microsoft Copilot (GPT-4–based)—to generate clinically appropriate recommendations for real cases of hemophilia with concurrent malignancy. Methods: Six consecutive adult cases of hemophilia and cancer,...