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dc.contributor.authorKalliokoski, Tuomo
dc.contributor.editorHuang, Yufei
dc.contributor.editorKurgan, Lukasz
dc.contributor.editorLuo, Feng
dc.contributor.editorHu, Xiaohua
dc.contributor.editorChen, Yidong
dc.contributor.editorDougherty, Edward
dc.contributor.editorKloczkowski, Andrzej
dc.contributor.editorLi, Yaohang
dc.date.accessioned2022-01-26T06:54:45Z
dc.date.available2022-01-26T06:54:45Z
dc.date.issued2021
dc.identifier.citationKalliokoski, T. (2021). Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data. In Y. Huang, L. Kurgan, F. Luo, X. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), <i>IEEE BIBM 2021 : Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine, December 9-12, 2021, Virtual Event</i> (pp. 3157-3164). IEEE. <a href="https://doi.org/10.1109/bibm52615.2021.9669525" target="_blank">https://doi.org/10.1109/bibm52615.2021.9669525</a>
dc.identifier.otherCONVID_103930024
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/79514
dc.description.abstractThere are multiple papers published about different AI models for the COVID-19 diagnosis with promising results. Unfortunately according to the reviews many of the papers do not reach the level of sophistication needed for a clinically usable model. In this paper I go through multiple review papers, guidelines, and other relevant material in order to generate more comprehensive requirements for the future papers proposing a AI based diagnosis of the COVID-19 from chest X-ray data (CXR). Main findings are that a clinically usable AI needs to have an extremely good documentation, comprehensive statistical analysis of the possible biases and performance, and an explainability module.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofIEEE BIBM 2021 : Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine, December 9-12, 2021, Virtual Event
dc.rightsIn Copyright
dc.subject.otherCOVID-19
dc.subject.otherstatistical analysis
dc.subject.otherdata models
dc.subject.otherclinical diagnosis
dc.subject.otherartificial intelligence
dc.titleRequirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202201261285
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-1-6654-0126-5
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange3157-3164
dc.type.versiondraft
dc.rights.copyright© 2021 IEEE
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceIEEE International Conference on Bioinformatics and Biomedicine
dc.subject.ysolääketiede
dc.subject.ysoCOVID-19
dc.subject.ysotilastolliset mallit
dc.subject.ysoennusteet
dc.subject.ysotekoäly
dc.subject.ysodiagnostiikka
dc.subject.ysotietomallit
dc.subject.ysotilastomenetelmät
dc.subject.ysotietojärjestelmät
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p469
jyx.subject.urihttp://www.yso.fi/onto/yso/p38829
jyx.subject.urihttp://www.yso.fi/onto/yso/p26278
jyx.subject.urihttp://www.yso.fi/onto/yso/p3297
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p416
jyx.subject.urihttp://www.yso.fi/onto/yso/p25167
jyx.subject.urihttp://www.yso.fi/onto/yso/p3127
jyx.subject.urihttp://www.yso.fi/onto/yso/p3927
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/bibm52615.2021.9669525
dc.type.okmA4


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