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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.<\/jats:p>","DOI":"10.1155\/2019\/1870975","type":"journal-article","created":{"date-parts":[[2019,4,3]],"date-time":"2019-04-03T00:57:23Z","timestamp":1554253043000},"page":"1-24","source":"Crossref","is-referenced-by-count":20,"title":["Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9697-666X","authenticated-orcid":true,"given":"Amina","family":"Adadi","sequence":"first","affiliation":[{"name":"Computer and Interdisciplinary Physics Laboratory, Sidi Mohamed Ben Abdellah University, Fez 30050, 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