Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence.

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Publisher: Elsevier B.V.
Document Type: Report
Length: 581 words

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Keywords AI; Artificial intelligence; Nursing; Nursing informatics; Scoping review Abstract Background Research on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare. Objectives To synthesize currently available state-of the-art research in artificial intelligence -based technologies applied in nursing practice. Design Scoping review Methods PubMed, CINAHL, Web of Science and IEEE Xplore were searched for relevant articles with queries that combine names and terms related to nursing, artificial intelligence and machine learning methods. Included studies focused on developing or validating artificial intelligence -based technologies with a clear description of their impacts on nursing. We excluded non-experimental studies and research targeted at robotics, nursing management and technologies used in nursing research and education. Results A total of 7610 articles published between January 2010 and March 2021 were revealed, with 93 articles included in this review. Most studies explored the technology development (n = 55, 59.1%) and formation (testing) (n = 28, 30.1%) phases, followed by implementation (n = 9, 9.7%) and operational (n = 1, 1.1%) phases. The vast majority (73.1%) of studies provided evidence with a descriptive design (level VI) while only a small portion (4.3%) were randomised controlled trials (level II). The study aims, settings and methods were poorly described in the articles, and discussion of ethical considerations were lacking in 36.6% of studies. Additionally, one-third of papers (33.3%) were reported without the involvement of nurses. Conclusions Contemporary research on applications of artificial intelligence -based technologies in nursing mainly cover the earlier stages of technology development, leaving scarce evidence of the impact of these technologies and implementation aspects into practice. The content of research reported is varied. Therefore, guidelines on research reporting and implementing artificial intelligence -based technologies in nursing are needed. Furthermore, integrating basic knowledge of artificial intelligence -related technologies and their applications in nursing education is imperative, and interventions to increase the inclusion of nurses throughout the technology research and development process is needed. Author Affiliation: (a) Department of Nursing Science University of Turku, Turku, Finland (b) Department of Computing, University of Turku, Turku, Finland (c) School of Nursing, University of British Columbia, Vancouver, Canada (d) Lawrence S. Bloomberg Faculty of Nursing. University of Toronto, Toronto, Canada (e) University of Hawaii at Maui, Hawaii, United States (f) Columbia University School of Nursing, United States (g) School of Nursing, University of Minnesota, Minneapolis, MN, United States (h) School of Computing and Mathematics, Keele University, United Kingdom (i) Fraser Health Authority, BC, Canada (j) Daphne Cockwell School of Nursing, Ryerson University, Toronto, Canada (k) School of Nursing, University of Minnesota, Minneapolis, United States (l) School of Nursing, University of British Columbia Okanagan, Kelowna, BC, Canada (m) School of Nursing, Columbia University, New York, United States * Corresponding author. Article History: Received 11 August 2021; Revised 23 November 2021; Accepted 1 December 2021 Byline: Hanna von Gerich [hanna.m.vongerich@utu.fi] (a), Hans Moen (b), Lorraine J. Block [lori.block@ubc.ca] (c), Charlene H. Chu [charlene.chu@utoronto.ca] (d), Haley DeForest (e), Mollie Hobensack [mxh2000@cumc.columbia.edu] (f), Martin Michalowski [martinm@umn.edu] (g), James Mitchell [j.a.mitchell@keele.ac.uk] (h), Raji Nibber [raji.nibber@alumni.ubc.ca] (i), Mary Anne Olalia [molalia@ryerson.ca] (j), Lisiane Pruinelli [pruin001@umn.edu] (k), Charlene E. Ronquillo [charlene.ronquillo@ubc.ca] (l), Maxim Topaz (f,m), Laura-Maria Peltonen [laura-maria.peltonen@utu.fi] (a,*)

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Gale Document Number: GALE|A695646041