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dc.contributor.authorHolzinger, Andreas
dc.contributor.authorDehmer, Matthias
dc.contributor.authorEmmert-Streib, Frank
dc.contributor.authorCucchiara, Rita
dc.contributor.authorAugenstein, Isabelle
dc.contributor.authorDel Ser, Javier
dc.contributor.authorSamek, Wojciech
dc.contributor.authorJurisica, Igor
dc.contributor.authorDíaz Rodríguez, Natalia Ana 
dc.date.accessioned2021-12-17T11:27:19Z
dc.date.available2021-12-17T11:27:19Z
dc.date.issued2021-11-12
dc.identifier.citationAndreas Holzinger... [et al.]. Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence, Information Fusion, Volume 79, 2022, Pages 263-278, ISSN 1566-2535, [https://doi.org/10.1016/j.inffus.2021.10.007]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/72110
dc.descriptionAndreas Holzinger acknowledges funding support from the Austrian Science Fund (FWF), Project: P-32554 explainable Artificial Intelligence and from the European Union's Horizon 2020 research and innovation program under grant agreement 826078 (Feature Cloud). This publication reflects only the authors' view and the European Commission is not responsible for any use that may be made of the information it contains; Natalia Diaz-Rodriguez is supported by the Spanish Government Juan de la Cierva Incorporacion contract (IJC2019-039152-I); Isabelle Augenstein's research is partially funded by a DFF Sapere Aude research leader grant; Javier Del Ser acknowledges funding support from the Basque Government through the ELKARTEK program (3KIA project, KK-2020/00049) and the consolidated research group MATHMODE (ref. T1294-19); Wojciech Samek acknowledges funding support from the European Union's Horizon 2020 research and innovation program under grant agreement No. 965221 (iToBoS), and the German Federal Ministry of Education and Research (ref. 01IS18025 A, ref. 01IS18037I and ref. 0310L0207C); Igor Jurisica acknowledges funding support from Ontario Research Fund (RDI 34876), Natural Sciences Research Council (NSERC 203475), CIHR Research Grant (93579), Canada Foundation for Innovation (CFI 29272, 225404, 33536), IBM, Ian Lawson van Toch Fund, the Schroeder Arthritis Institute via the Toronto General and Western Hospital Foundation.es_ES
dc.description.abstractMedical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant.es_ES
dc.description.sponsorshipAustrian Science Fund (FWF) P-32554es_ES
dc.description.sponsorshipEuropean Union's Horizon 2020 research and innovation program 826078 965221es_ES
dc.description.sponsorshipSpanish Government Juan de la Cierva Incorporacion IJC2019-039152-Ies_ES
dc.description.sponsorshipDFF Sapere Aude research leader grantes_ES
dc.description.sponsorshipBasque Government KK-2020/00049es_ES
dc.description.sponsorshipconsolidated research group MATHMODE T1294-19es_ES
dc.description.sponsorshipFederal Ministry of Education & Research (BMBF) 01IS18025 A 01IS18037I 0310L0207Ces_ES
dc.description.sponsorshipOntario Research Fund RDI 34876es_ES
dc.description.sponsorshipNatural Sciences Research Council NSERC 203475es_ES
dc.description.sponsorshipCanadian Institutes of Health Research (CIHR) 93579es_ES
dc.description.sponsorshipCanada Foundation for Innovation CGIAR CFI 29272 225404 33536es_ES
dc.description.sponsorshipInternational Business Machines (IBM)es_ES
dc.description.sponsorshipIan Lawson van Toch Fundes_ES
dc.description.sponsorshipSchroeder Arthritis Institute via the Toronto General and Western Hospital Foundationes_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectArtificial intelligence es_ES
dc.subjectInformation fusiones_ES
dc.subjectMedical AIes_ES
dc.subjectExplainable AIes_ES
dc.subjectRobustnesses_ES
dc.subjectExplainabilityes_ES
dc.subjectTrustes_ES
dc.subjectGraph-based machine learninges_ES
dc.subjectNeural-symbolic learning and reasoninges_ES
dc.titleInformation fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligencees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/826078es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/965221es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1016/j.inffus.2021.10.007
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España