{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:33:46Z","timestamp":1787924026988,"version":"build-2784847793"},"reference-count":0,"publisher":"Walter de Gruyter GmbH","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GlaxoSmithKline"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Eur. J. Health Law"],"published-print":{"date-parts":[[2026,8,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This paper examines how privacy-enhancing technologies such as synthetic data, federated learning and \u2018Secure Data Environments\u2019 can be integrated into artificial intelligence (\n                    <jats:sc>AI<\/jats:sc>\n                    ) development processes to uphold key data protection principles in the UK\n                    <jats:sc>GDPR<\/jats:sc>\n                    , like storage limitation, data minimisation, purpose limitation, security, and fairness. The analysis highlights how privacy-enhancing technologies offer benefits beyond anonymisation by embedding privacy-by-design values to support responsible innovation and protect sensitive patient data throughout the design, training, and validation of medical\n                    <jats:sc>AI<\/jats:sc>\n                    systems. The paper uses the 2015 DeepMind and Royal Free case as a practical study to realise the practical and legal benefits of privacy-enhancing technologies in medical\n                    <jats:sc>AI<\/jats:sc>\n                    development, particularly involving public-private collaborations. While grounded in the UK context, the findings have broader relevance to the European Union and other international jurisdictions grappling with tensions between data protection and\n                    <jats:sc>AI<\/jats:sc>\n                    development in the healthcare context.\n                  <\/jats:p>","DOI":"10.1163\/15718093-bja10176","type":"journal-article","created":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T03:04:31Z","timestamp":1786763071000},"page":"1-26","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-Enhancing Technologies and Privacy-By-Design for Medical AI"],"prefix":"10.1163","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5289-9219","authenticated-orcid":true,"given":"Yasmine","family":"Zoya","sequence":"first","affiliation":[{"name":"GSK.ai, GSK King\u2019s Cross, London UK"},{"name":"Faculty of Law, University of Oxford https:\/\/dx.doi.org\/6396 St Cross Building St. Cross Road, Oxford OX1 3UL UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","container-title":["European Journal of Health Law"],"original-title":[],"link":[{"URL":"https:\/\/brill.com\/view\/journals\/ejhl\/aop\/article-10.1163-15718093-bja10176\/article-10.1163-15718093-bja10176.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/brill.com\/downloadpdf\/journals\/ejhl\/aop\/article-10.1163-15718093-bja10176\/article-10.1163-15718093-bja10176.xml","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:07:27Z","timestamp":1787922447000},"score":1,"resource":{"primary":{"URL":"https:\/\/brill.com\/view\/journals\/ejhl\/aop\/article-10.1163-15718093-bja10176\/article-10.1163-15718093-bja10176.xml"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1163\/15718093-bja10176","relation":{},"ISSN":["0929-0273","1571-8093"],"issn-type":[{"value":"0929-0273","type":"print"},{"value":"1571-8093","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]}}}