Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis. (2nd April 2019)

• Record Type: Journal Article
• Authors: Adadi, Amina; Adadi, Safae; Berrada, Mohammed
• Other Names: Fdez-Riverola Florentino Academic Editor.
• Is Part Of: Advances in bioinformatics. Volume 2019(2019)
• Publication Date: 2019-04-02
• Subjects: Bioinformatics -- Periodicals; Bioinformatics; Computational Biology -- Periodicals; Periodicals; 570.285
• DOI: 10.1155/2019/1870975
• Languages: English
• ISSNs: 1687-8027

Abstract

Abstract : Machine learning has undergone a transition phase from being a pure statistical tool to being one of the main drivers of modern medicine. In gastroenterology, this technology is motivating a growing number of studies that rely on these innovative methods to deal with critical issues related to this practice. Hence, in the light of the burgeoning research on the use of machine learning in gastroenterology, a systematic review of the literature is timely. In this work, we present the results gleaned through a systematic review of prominent gastroenterology literature using machine learning techniques. Based on the analysis of 88 journal articles, we delimit the scope of application, we discuss current limitations including bias, lack of transparency, accountability, and data availability, and we put forward future avenues.