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Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness
21 December 2018
Sebastian J. Vollmer
Bilal A. Mateen
G. Bohner
Franz J. Király
Rayid Ghani
P. Jónsson
Sarah Cumbers
Adrian Jonas
K. McAllister
P. Myles
David Granger
Mark Birse
Richard Branson
Karel G. M. Moons
Gary S. Collins
J. Ioannidis
Chris Holmes
H. Hemingway
Re-assign community
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Papers citing
"Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness"
4 / 4 papers shown
Title
The Response Shift Paradigm to Quantify Human Trust in AI Recommendations
A. Shafti
Victoria Derks
Hannah Kay
Aldo A. Faisal
22
7
0
16 Feb 2022
Stratified cross-validation for unbiased and privacy-preserving federated learning
R. Bey
Romain Goussault
M. Benchoufi
R. Porcher
FedML
29
12
0
22 Jan 2020
A new direction to promote the implementation of artificial intelligence in natural clinical settings
Yunyou Huang
Zhifei Zhang
Nana Wang
Nengquan Li
M. Du
Tianshu Hao
Jianfeng Zhan
11
2
0
08 May 2019
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
266
7,640
0
03 Jul 2012
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