The reuse of health data and the application of artificial intelligence (AI) hold significant promise for advancing personalised and precision medicine, yet their implementation in clinical practice and research remains constrained by technical, legal, ethical, and organisational barriers. This talk examines these challenges through two complementary case studies: AI-driven decision support for multiple sclerosis using multimodal real-world data, and the secondary use of complex clinical and biomedical data in neuro-oncological research.
Bringing together perspectives from data science and data protection law, the speakers explore the persistent gap between technological innovation and practical deployment. Key obstacles include fragmented data infrastructures, limited interoperability, strict interpretations of data protection and medical secrecy, governance complexity, and the evolving European regulatory landscape, notably the GDPR and the European Health Data Space (EHDS).
The talk will examine emerging technical and governance solutions, including federated learning, FHIR-based interoperability, secure data environments, and European initiatives aimed at enabling responsible health data reuse. By bringing together empirical experience from hospital practice and interdisciplinary research with legal-ethical analysis, this session aims to provide participants with a nuanced understanding of how AI and health data governance can be aligned to deliver clinically meaningful, ethically grounded, and legally compliant innovation for patient benefit in Europe.