Editorial: ML and AI Safety, Effectiveness and Explainability in Healthcare

David Benrimoh; Sonia Israel; Robert Fratila; Caitrin Armstrong; Kelly Perlman; Ariel Rosenfeld; Adam Kapelner

doi:10.3389/fdata.2021.727856

Editorial: ML and AI Safety, Effectiveness and Explainability in Healthcare

David Benrimoh
, Sonia Israel
, Robert Fratila
, Caitrin Armstrong
, Kelly Perlman
, Ariel Rosenfeld
, Adam Kapelner

Department of Information Science and Applied Artificial Intelligence

Bar-Ilan University

Research output: Contribution to journal › Editorial

Original language	English
Article number	727856
Journal	Frontiers in Big Data
Volume	4
DOIs	https://doi.org/10.3389/fdata.2021.727856
State	Published - 12 Jul 2021

Keywords

artificial inteligence
effectiveness
explainability
healthcare
machine learning
safety

ASJC Scopus subject areas

Computer Science (miscellaneous)
Information Systems
Artificial Intelligence

Access to Document

10.3389/fdata.2021.727856

Cite this

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keywords = "artificial inteligence, effectiveness, explainability, healthcare, machine learning, safety",

author = "David Benrimoh and Sonia Israel and Robert Fratila and Caitrin Armstrong and Kelly Perlman and Ariel Rosenfeld and Adam Kapelner",

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language = "English",

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AU - Benrimoh, David

AU - Israel, Sonia

AU - Fratila, Robert

AU - Armstrong, Caitrin

AU - Perlman, Kelly

AU - Rosenfeld, Ariel

AU - Kapelner, Adam

PY - 2021/7/12

Y1 - 2021/7/12

KW - artificial inteligence

KW - effectiveness

KW - explainability

KW - healthcare

KW - machine learning

KW - safety

UR - https://www.scopus.com/pages/publications/85111419626

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DO - 10.3389/fdata.2021.727856

M3 - Editorial

C2 - 34322667

VL - 4

JO - Frontiers in Big Data

JF - Frontiers in Big Data

M1 - 727856

ER -

Editorial: ML and AI Safety, Effectiveness and Explainability in Healthcare

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this