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dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorStensen, Mette Haug
dc.contributor.authorWitczak, Oliwia
dc.contributor.authorAndersen, Jorunn Marie
dc.contributor.authorHicks, Steven
dc.contributor.authorHammer, Hugo Lewi
dc.contributor.authorDelbarre, Erwan
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorYazidi, Anis
dc.contributor.authorHolst, Nicolai
dc.contributor.authorHaugen, Trine B.
dc.date.accessioned2022-03-10T09:30:55Z
dc.date.available2022-03-10T09:30:55Z
dc.date.created2021-11-08T09:47:44Z
dc.date.issued2021
dc.identifier.issn0268-1161
dc.identifier.urihttps://hdl.handle.net/11250/2984175
dc.description.abstractIn recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome. In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.en_US
dc.language.isoengen_US
dc.publisherOxford University Pressen_US
dc.titleArtificial intelligence in the fertility clinic: status, pitfalls and possibilitiesen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderThe accepted version of this article will not be available until July 29, 2022 due to publisher embargoen_US
dc.source.journalHuman Reproductionen_US
dc.identifier.doi10.1093/humrep/deab168
dc.identifier.cristin1952194
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextpostprint
cristin.qualitycode2


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