Machine Learning Operations in Health Care: A Scoping Review

Abstract

The use of machine learning tools in health care is rapidly expanding. However, the processes that support these tools in deployment, that is, machine learning operations, are still emerging. The purpose of this work was not only to provide a comprehensive synthesis of existing literature in the field but also to identify gaps and offer insights for adoption in clinical practice. A scoping review was conducted using the MEDLINE, PubMed, Google Scholar, Embase, and Scopus databases. We used MeSH and non-MeSH search terms to identify pertinent articles, with the authors performing 2 screening phases and assigning relevance scores: 148 English language articles most salient to the review were eligible for inclusion; 98 offered the most unique information and these were supplemented by 50 additional sources, yielding 148 references. From the 148 references, we distilled 7 key topic areas, based on a synthesis of the available literature and how that aligned with practitioner needs. The 7 topic areas were machine learning model monitoring; automated retraining systems; ethics, equity, and bias; clinical workflow integration; infrastructure, human resources, and technology stack; regulatory considerations; and financial considerations. This review provides an overview of best practices and knowledge gaps of this domain in health care and identifies the strengths and weaknesses of the literature, which may be useful to health care machine learning practitioners and consumers.