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

• DOI: 10.1016/j.ijnurstu.2021.104153
• Publication Date: 2021-12-08T00:06:56Z
• Authors: Hanna von Gerich; Hans Moen; Lorraine J. Block; Charlene H. Chu; Haley DeForest; Mollie Hobensack; Martin Michalowski; James Mitchell; Raji Nibber; Mary Anne Olalia; Lisiane Pruinelli; Charlene E. Ronqu...; Maxim Topaz; Laura-Maria Peltonen
• Keywords: CINAHL

Research on technologies based artificial intelligence in healthcare has increased during the last decade, with applications showing great potential assisting and improving care. However, introducing these into nursing can raise concerns related to data bias context of training algorithms implications for certain populations. Little evidence exists extant literature regarding efficacious application many -based health used healthcare. To synthesize currently available state-of the-art research applied practice. Scoping review PubMed, CINAHL, Web Science IEEE Xplore were searched relevant articles queries that combine names terms nursing, machine learning methods. Included studies focused developing or validating a clear description their impacts nursing. We excluded non-experimental targeted at robotics, management education. A total 7610 published between January 2010 March 2021 revealed, 93 included this review. Most explored technology development (n = 55, 59.1%) formation (testing) 28, 30.1%) phases, followed by implementation 9, 9.7%) operational 1, 1.1%) phases. The vast majority (73.1%) provided descriptive design (level VI) while only small portion (4.3%) randomised controlled trials II). study aims, settings methods poorly described articles, discussion ethical considerations lacking 36.6% studies. Additionally, one-third papers (33.3%) reported without involvement nurses. Contemporary mainly cover earlier stages development, leaving scarce impact aspects content is varied. Therefore, guidelines reporting implementing are needed. Furthermore, integrating basic knowledge -related education imperative, interventions increase inclusion nurses throughout process