Adoption of machine learning systems within the health sector: a systematic review, synthesis and research agenda

Doreen Nkirote Bundi

Digital Transformation and Society, 2023, vol. 3, issue 1, 99-120

Abstract: Purpose - The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward. Design/methodology/approach - A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed. Findings - The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area. Research limitations/implications - The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation. Originality/value - This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

Keywords: Health sector; Machine learning; Machine learning systems (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eme:dtspps:dts-06-2023-0041

DOI: 10.1108/DTS-06-2023-0041

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