Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions-Reference-Cited by-同舟云学术

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

Published:2022 Issue: Volume:10 Page:58375-58418
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Petersen Eike¹^ORCID,Potdevin Yannik²,Mohammadi Esfandiar³^ORCID,Zidowitz Stephan⁴^ORCID,Breyer Sabrina⁵^ORCID,Nowotka Dirk²^ORCID,Henn Sandra⁵^ORCID,Pechmann Ludwig⁶^ORCID,Leucker Martin⁶^ORCID,Rostalski Philipp⁵^ORCID,Herzog Christian⁵^ORCID

Affiliation:

1. DTU Compute, Technical University of Denmark, Lyngby, Denmark

2. Department of Computer Science, Kiel University, Kiel, Germany

3. Institute for IT Security (ITS), Universität zu Lübeck, Lübeck, Germany

4. Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany

5. Institute for Electrical Engineering in Medicine (IME), Universität zu Lübeck, Lübeck, Germany

6. UniTransferKlinik Lübeck GmbH, Lübeck, Germany

Funder

German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the AI Space for Intelligent Health Systems (KI-SIGS) Project

several universities, research institutions, and private companies in northern Germany

Academy of Sciences and Humanities, Hamburg

KI-SIGS Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09783196.pdf?arnumber=9783196

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5. Tutorial: Safe and reliable machine learning;saria;Proc ACM Conf Fairness Accountability Transparency (ACM FAT*),2019

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