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Synthetic data in medical imaging within the EHDS

2025-10Recensão Acesso aberto
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dc.contributor.authorJiang, Junying
dc.contributor.authorDomingues, Lúcia
dc.contributor.authorDomingues, Lúcia
dc.contributor.authorMendes, Jorge M.
dc.contributor.authorMendes, Jorge M.
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - pólo NMS
dc.contributor.pblFrontiers Media
dc.date.accessioned2025-11-18T21:10:55Z
dc.date.available2025-11-18T21:10:55Z
dc.date.issued2025-10
dc.descriptionFunding Information: The author(s) declare that financial support was received for the research and/or publication of this article. The present publication was funded by Funda\u00E7\u00E3o Ci\u00EAncia e Tecnologia, IP national support through UID/04923 \u2013 Comprehensive Health Research Centre. Publisher Copyright: 2025 Jiang, Domingues and Mendes.
dc.description.abstractThe increasing availability of medical imaging data offers unprecedented opportunities for advancing artificial intelligence (AI)-driven healthcare. However, strict data protection regulations in the European Union (EU), especially the General Data Protection Regulation (GDPR), present significant challenges to data sharing and reuse. Synthetic data—artificially generated data that mimic the statistical properties of real data without revealing sensitive information—have emerged as a promising solution to bridge this gap. This perspective-style review examines the role of synthetic medical imaging data within the European Health Data Space (EHDS), a policy initiative aimed at enabling secure access to health data across the EU. While we briefly reference cross-cutting privacy-enhancing technologies and one non-imaging comparator to illuminate shared governance issues, our analysis and conclusions are scoped to imaging applications. We discuss the technical foundations and types of synthetic data, their potential to enhance reproducibility and innovation, and the complex ethical and legal concerns surrounding their use. Emphasising the need for a risk-based regulatory framework, we advocate for synthetic data governance that ensures utility, transparency, and accountability, especially when such data are generated using generative AI models. This work contributes to ongoing debates on how synthetic imaging data can support a privacy-preserving, data-driven healthcare ecosystem in Europe.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent804491
dc.identifier.doi10.3389/fdgth.2025.1620270
dc.identifier.issn2673-253X
dc.identifier.otherPURE: 133540348
dc.identifier.otherPURE UUID: 8d89ab65-2773-4130-8f6c-dcef994cf311
dc.identifier.otherScopus: 105019375230
dc.identifier.otherPubMed: 41132396
dc.identifier.urihttp://hdl.handle.net/10362/191006
dc.identifier.urlhttps://www.scopus.com/pages/publications/105019375230
dc.language.isoeng
dc.peerreviewedyes
dc.subjectAI act
dc.subjectdata privacy
dc.subjectEuropean Health Data Space (EHDS)
dc.subjectGDPR compliance
dc.subjectgenerative AI
dc.subjectMedical Device Regulation (MDR)
dc.subjectmedical imaging
dc.subjectsynthetic data
dc.subjectMedicine (miscellaneous)
dc.subjectBiomedical Engineering
dc.subjectHealth Informatics
dc.subjectComputer Science Applications
dc.titleSynthetic data in medical imaging within the EHDSen
dc.title.subtitlea path forward for ethics, regulation, and standardsen
dc.typereview
degois.publication.titleFrontiers in Digital Health
degois.publication.volume7
dspace.entity.typePublication
person.familyNameDomingues
person.familyNameMendes
person.givenNameLúcia
person.givenNameJorge M.
person.identifier3189571
person.identifier.ciencia-id7A10-3F00-9118
person.identifier.ciencia-id5411-CF11-16E0
person.identifier.orcid0000-0003-3341-8270
person.identifier.orcid0000-0003-2251-3803
person.identifier.ridE-3289-2010
person.identifier.scopus-author-id56487343100
person.identifier.scopus-author-id7006448639
rcaap.rightsopenAccess
relation.isAuthorOfPublication7459e6bd-3ede-455d-a52a-f797ef1bf40e
relation.isAuthorOfPublication6baa0541-0393-4584-a98a-35ce87f4bd1a
relation.isAuthorOfPublication.latestForDiscovery7459e6bd-3ede-455d-a52a-f797ef1bf40e

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