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.accessioned2023-04-11T07:54:05Z
dc.date.available2023-04-11T07:54:05Z
dc.date.issued2021-11-02
dc.date.updated2023-03-25T17:29:39Z
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 challenging 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 effective 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.en
dc.identifier.eissn1573-689X
dc.identifier.issn0148-5598
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18555
dc.identifier.urihttps://doi.org/10.14279/depositonce-17364
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.subject.ddc000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung; Informatik
dc.subject.othermachine learningen
dc.subject.otherartificial intelligenceen
dc.subject.otheralgorithmen
dc.subject.otherhealthen
dc.subject.otherauditingen
dc.subject.otherquality controlen
dc.titleMachine Learning for Health: Algorithm Auditing & Quality Controlen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber105
dcterms.bibliographicCitation.doi10.1007/s10916-021-01783-y
dcterms.bibliographicCitation.issue12
dcterms.bibliographicCitation.journaltitleJournal of Medical Systems
dcterms.bibliographicCitation.originalpublishernameSpringer Nature
dcterms.bibliographicCitation.originalpublisherplaceHeidelberg
dcterms.bibliographicCitation.volume45
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Unsicherheit, inverse Modellierung und maschinelles Lernen
tub.publisher.universityorinstitutionTechnische Universität Berlin

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