Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation

Damien Gruson; Thibault Helleputte; Patrick Rousseau; David Gruson

doi:10.1016/j.clinbiochem.2019.04.013

Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation

Clin Biochem. 2019 Jul:69:1-7. doi: 10.1016/j.clinbiochem.2019.04.013. Epub 2019 Apr 22.

Authors

Damien Gruson¹, Thibault Helleputte², Patrick Rousseau³, David Gruson⁴

Affiliations

¹ Department of Laboratory Medicine, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium; Pôle de recherche en Endocrinologie, Diabète et Nutrition, Institut de Recherche Expérimentale et Clinique, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium. Electronic address: [email protected].
² Data-Driven Precision Medicine, DNAlytics, Louvain-la-Neuve, Belgium.
³ Department of Laboratory Medicine, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium.
⁴ Genetics Regulation for Paris Descartes-University, Paris, France.

PMID: 31022391
DOI: 10.1016/j.clinbiochem.2019.04.013

Abstract

Artificial intelligence (AI) and data science are rapidly developing in healthcare, as is their translation into laboratory medicine. Our review article presents an overview of the data science domain while discussing the reasons for its emergence. We also present several perspectives of its applications in clinical laboratories, along with potential ethical challenges related to AI and data science.

Keywords: Artificial intelligence; Big data; Biomarker; Deep learning; Ethics; Health; Machine learning.

Publication types

Review

MeSH terms

Artificial Intelligence*
Big Data
Data Science*
Humans
Machine Learning*
Medical Laboratory Science*