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Addressing bias in big data and AI for health care: A call for open science

Abordando el sesgo en los grandes datos y la IA para la atención médica: Un llamado a la ciencia abierta
descriptionPublicationkeyboard_double_arrow_right Article , Presentation , Other literature type 01 Oct 2021 Switzerland English Publisher:Elsevier BVJournal:Patterns, volume 2, page 100,347 (issn: 2666-3899, Copyright policy )Funded by:SNSF | Sensory predictions in th...
Authors: Natalia Norori; Qiyang Hu; Florence Marcelle Aellen; Francesca Dalia Faraci; Athina Tzovara;

doi: 10.1016/j.patter.2021.100347 , 10.5281/zenodo.4767785 , 10.5281/zenodo.4767786 , 10.48350/161897

pmid: 34693373

pmc: PMC8515002

Addressing bias in big data and AI for health care: A call for open science

Abstract

Presentation: Addressing bias in big data and AI for healthcare: a call for open science

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Switzerland
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Keywords

bias, participatory science, deep learning, 610 Medicine & health, Deep learning, 000 Computer science, knowledge & systems, health care, 510 Mathematics, Artificial Intelligence, open science, Perspective, data standards

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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OpenAIRE UsageCountsViews provided by UsageCounts
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