Towards trustable machine learning View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-10

ABSTRACT

Clinical implementations of machine learning that are accurate, robust and interpretable will eventually gain the trust of healthcare providers and patients.

PAGES

709-710

Journal

TITLE

Nature Biomedical Engineering

ISSUE

10

VOLUME

2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41551-018-0315-x

DOI

http://dx.doi.org/10.1038/s41551-018-0315-x

DIMENSIONS

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