A manifesto on explainability for artificial intelligence in medicine.
Artificial intelligence
Explainability
Explainable artificial intelligence
Interpretability
Interpretable artificial intelligence
Journal
Artificial intelligence in medicine
ISSN: 1873-2860
Titre abrégé: Artif Intell Med
Pays: Netherlands
ID NLM: 8915031
Informations de publication
Date de publication:
11 2022
11 2022
Historique:
received:
02
03
2022
revised:
04
10
2022
accepted:
04
10
2022
entrez:
3
11
2022
pubmed:
4
11
2022
medline:
8
11
2022
Statut:
ppublish
Résumé
The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine.
Identifiants
pubmed: 36328669
pii: S0933-3657(22)00175-0
doi: 10.1016/j.artmed.2022.102423
pii:
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
102423Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR001878
Pays : United States
Informations de copyright
Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.