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2021-11-02Zeitschriftenartikel
Machine Learning for Health: Algorithm Auditing & Quality Control
dc.contributor.authorOala, Luis
dc.contributor.authorMurchison, Andrew G.
dc.contributor.authorBalachandran, Pradeep
dc.contributor.authorChoudhary, Shruti
dc.contributor.authorFehr, Jana
dc.contributor.authorLeite, Alixandro Werneck
dc.contributor.authorGoldschmidt, Peter G.
dc.contributor.authorJohner, Christian
dc.contributor.authorSchörverth, Elora D. M.
dc.contributor.authorNakasi, Rose
dc.contributor.authorMeyer, Martin
dc.contributor.authorCabitza, Federico
dc.contributor.authorBaird, Pat
dc.contributor.authorPrabhu, Carolin
dc.contributor.authorWeicken, Eva
dc.contributor.authorLiu, Xiaoxuan
dc.contributor.authorWenzel, Markus
dc.contributor.authorVogler, Steffen
dc.contributor.authorAkogo, Darlington
dc.contributor.authorAlsalamah, Shada
dc.contributor.authorKazim, Emre
dc.contributor.authorKoshiyama, Adriano
dc.contributor.authorPiechottka, Sven
dc.contributor.authorMacpherson, Sheena
dc.contributor.authorShadforth, Ian
dc.contributor.authorGeierhofer, Regina
dc.contributor.authorMatek, Christian
dc.contributor.authorKrois, Joachim
dc.contributor.authorSanguinetti, Bruno
dc.contributor.authorArentz, Matthew
dc.contributor.authorBielik, Pavol
dc.contributor.authorCalderon‑Ramirez, Saul
dc.contributor.authorAbbood, Auss
dc.contributor.authorLanger, Nicolas
dc.contributor.authorHaufe, Stefan
dc.contributor.authorKherif, Ferath
dc.contributor.authorPujari, Sameer
dc.contributor.authorSamek, Wojciech
dc.contributor.authorWiegand, Thomas
dc.date.accessioned2022-03-14T12:17:32Z
dc.date.available2022-03-14T12:17:32Z
dc.date.issued2021-11-02none
dc.identifier.other10.1007/s10916-021-01783-y
dc.identifier.urihttp://edoc.rki.de/176904/9498
dc.description.abstractDevelopers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challeng ing as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the efective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.eng
dc.language.isoengnone
dc.publisherRobert Koch-Institut
dc.rights(CC BY 3.0 DE) Namensnennung 3.0 Deutschlandger
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/de/
dc.subjectMachine learningeng
dc.subjectArtifcial intelligenceeng
dc.subjectAlgorithmeng
dc.subjectHealtheng
dc.subjectAuditingeng
dc.subjectQuality controleng
dc.subject.ddc610 Medizin und Gesundheitnone
dc.titleMachine Learning for Health: Algorithm Auditing & Quality Controlnone
dc.typearticle
dc.identifier.urnurn:nbn:de:0257-176904/9498-0
dc.type.versionpublishedVersionnone
local.edoc.container-titleJournal of Medical Systemsnone
local.edoc.container-issn1573-689Xnone
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-urlhttps://link.springer.com/article/10.1007/s10916-021-01783-ynone
local.edoc.container-publisher-nameSpringer New Yorknone
local.edoc.container-volume45none
local.edoc.container-issue105none
local.edoc.container-year2021none
dc.description.versionPeer Reviewednone

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