Show simple item record

dc.contributor.authorKanwal, Neel
dc.contributor.authorJanssen, Emiel
dc.contributor.authorEngan, Kjersti
dc.date.accessioned2024-09-11T11:18:40Z
dc.date.available2024-09-11T11:18:40Z
dc.date.created2024-02-26T08:47:11Z
dc.date.issued2024
dc.identifier.citationKanwal, N., Janssen, E.A.M., Engan, K. (2024). Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education. In: Farmanbar, M., Tzamtzi, M., Verma, A.K., Chakravorty, A. (eds) Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications. FAIEMA 2023. Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications. Springer, Singapore.en_US
dc.identifier.isbn978-981-99-9836-4
dc.identifier.urihttps://hdl.handle.net/11250/3151438
dc.description.abstractThe advancement of biomedical research heavily relies on access to large amounts of medical data. In the case of histopathology, Whole Slide Images (WSI) and clinicopathological information are valuable for developing Artificial Intelligence (AI) algorithms for Digital Pathology (DP). Transferring medical data "as open as possible" enhances the usability of the data for secondary purposes but poses a risk to patient privacy. At the same time, existing regulations push towards keeping medical data "as closed as necessary" to avoid re-identification risks. Generally, these legal regulations require the removal of sensitive data but do not consider the possibility of data linkage attacks due to modern image-matching algorithms. In addition, the lack of standardization in DP makes it harder to establish a single solution for all formats of WSIs. These challenges raise problems for bio-informatics researchers in balancing privacy and progress while developing AI algorithms. This paper explores the legal regulations and terminologies for medical data-sharing. We review existing approaches and highlight challenges from the histopathological perspective. We also present a data-sharing guideline for histological data to foster multidisciplinary research and education.en_US
dc.language.isoengen_US
dc.publisherSpringer Nature Switzerland AGen_US
dc.relation.ispartofFrontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications
dc.relation.ispartofseriesFrontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications;
dc.relation.urihttps://arxiv.org/pdf/2307.09426.pdf
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.no*
dc.subjectAIen_US
dc.subjectartificial intelligenceen_US
dc.subjectKIen_US
dc.subjectkunstig intelligensen_US
dc.subjectanonymiseringen_US
dc.subjectbiomedisinsk forskningen_US
dc.subjectmedisinske dataen_US
dc.titleBalancing privacy and progress in artificial intelligence : Anonymization in histopathology for biomedical research and educationen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.source.pagenumber417-429en_US
dc.identifier.doi10.1007/978-981-99-9836-4_31
dc.identifier.cristin2249610
dc.relation.projectEC/H2020/860627en_US
cristin.ispublishedtrue
cristin.fulltextpostprint


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Navngivelse-Ikkekommersiell 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse-Ikkekommersiell 4.0 Internasjonal