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Machine Learning may push research in precision medicine to unprecedented heights. To succeed, machine learning needs a large amount of data, often including personal data. Therefore, machine learning applied to precision medicine is on a cliff edge: if it does not learn to fly, it will deeply fall down. In this paper, we present Active Informed Consent (AIC) as a novel hybrid legal-technological tool to foster the gathering of a large amount of data for machine learning. We carefully analyzed the compliance of this technological tool to the legal intricacies protecting the privacy of European Citizens.

Details

1009240
Subject
Machine learning;
Informed consent
Identifier / keyword
Computers and Society; Artificial Intelligence; Machine Learning
URL
http://arxiv.org/abs/2210.08987
Title
Active Informed Consent to Boost the Application of Machine Learning in Medicine
Author
Gerardi, Marco; Barud, Katarzyna; Wagner, Marie-Catherine; Forgo, Nikolaus; Fallucchi, Francesca; Scarpato, Noemi; Guadagni, Fiorella; Zanzotto, Fabio Massimo
Publication title
arXiv.org; Ithaca
Publication year
2022
Publication date
Sep 27, 2022
Section
Computer Science
Publisher
Cornell University Library, arXiv.org
Source
arXiv.org
Place of publication
Ithaca
Country of publication
United States
University/institution
Cornell University Library arXiv.org
Publication subject
Biology, Statistics, Physics, Mathematics, Engineering--Electrical Engineering, Computers--Computer Engineering, Business And Economics--Banking And Finance
e-ISSN
2331-8422
Source type
Working Paper
Language of publication
English
Document type
Working Paper
Publication history
 
 
Online publication date
2022-10-18
Milestone dates
2022-09-27 (Submission v1)
Publication history
 
 
   First posting date
18 Oct 2022
ProQuest document ID
2725734286
Document URL
https://www.proquest.com/working-papers/active-informed-consent-boost-application-machine/docview/2725734286/se-2?accountid=208611
Full text outside of ProQuest
http://arxiv.org/abs/2210.08987
Copyright
© 2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2022-10-19
Database
ProQuest One Academic