Reference : Translational Challenges of Biomedical Machine Learning Solutions in Clinical and Lab...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/51741

Title :	Translational Challenges of Biomedical Machine Learning Solutions in Clinical and Laboratory Settings
Language :	English
Author, co-author :	Vega Moreno, Carlos Gonzalo [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
	Kratochvil, Miroslav [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
	Satagopam, Venkata [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
	Schneider, Reinhard [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
Publication date :	2022
Main document title :	Bioinformatics and Biomedical Engineering
Publisher :	Springer International Publishing
Pages :	353--358
Peer reviewed :	Yes
Audience :	International
ISBN :	978-3-031-07802-6
City :	Cham
Event name :	International Work-Conference on Bioinformatics and Biomedical Engineering
Event date :	from 27-06-2022 to 30-06-2022
Event place (city) :	Meloneras
Event country :	Spain
Keywords :	[en] machine learning ; biomedicine
Abstract :	[en] The ever increasing use of artificial intelligence (AI) methods in biomedical sciences calls for closer inter-disciplinary collaborations that transfer the domain knowledge from life scientists to computer science researchers and vice-versa. We highlight two general areas where the use of AI-based solutions designed for clinical and laboratory settings has proven problematic. These are used to demonstrate common sources of translational challenges that often stem from the differences in data interpretation between the clinical and research view, and the unmatched expectations and requirements on the result quality metrics. We outline how explicit interpretable inference reporting might be used as a guide to overcome such translational challenges. We conclude with several recommendations for safer translation of machine learning solutions into real-world settings.
Permalink :	http://hdl.handle.net/10993/51741
DOI :	10.1007/978-3-031-07802-6_30
Other URL :	https://link.springer.com/chapter/10.1007/978-3-031-07802-6_30

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