Causability and explainability of artificial intelligence in medicine.
artificial intelligence
causability
explainability
explainable AI
histopathology
medicine
Journal
Wiley interdisciplinary reviews. Data mining and knowledge discovery
ISSN: 1942-4787
Titre abrégé: Wiley Interdiscip Rev Data Min Knowl Discov
Pays: United States
ID NLM: 101567990
Informations de publication
Date de publication:
Historique:
received:
19
11
2018
revised:
26
01
2019
accepted:
24
02
2019
entrez:
25
2
2020
pubmed:
25
2
2020
medline:
25
2
2020
Statut:
ppublish
Résumé
Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI deals with the implementation of transparency and traceability of statistical black-box machine learning methods, particularly deep learning (DL). We argue that there is a need to go beyond explainable AI. To reach a level of
Identifiants
pubmed: 32089788
doi: 10.1002/widm.1312
pii: WIDM1312
pmc: PMC7017860
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
e1312Subventions
Organisme : Austrian Science Fund FWF
ID : I 2714
Pays : Austria
Informations de copyright
© 2019 The Authors. WIREs Data Mining and Knowledge Discovery published by Wiley Periodicals, Inc.
Déclaration de conflit d'intérêts
The authors have declared no conflicts of interest for this article.
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