Reference : Translational Challenges of Biomedical Machine Learning Solutions in Clinical and Lab...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Systems Biomedicine
http://hdl.handle.net/10993/51741
Translational Challenges of Biomedical Machine Learning Solutions in Clinical and Laboratory Settings
English
Vega Moreno, Carlos Gonzalo mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
Kratochvil, Miroslav mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
Satagopam, Venkata mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
Schneider, Reinhard mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
2022
Bioinformatics and Biomedical Engineering
Springer International Publishing
353--358
Yes
International
978-3-031-07802-6
Cham
International Work-Conference on Bioinformatics and Biomedical Engineering
from 27-06-2022 to 30-06-2022
Meloneras
Spain
[en] machine learning ; biomedicine
[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.
http://hdl.handle.net/10993/51741
10.1007/978-3-031-07802-6_30
https://link.springer.com/chapter/10.1007/978-3-031-07802-6_30

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Limited access
Translational_challenges_of_biomedical_ML___IWBBIO_2022.pdfAuthor postprint189.26 kBRequest a copy

Additional material(s):

File Commentary Size Access
Open access
poster_nldl22 (1).pdfPoster6.6 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.