Background Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. Objective This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. Methods Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. Results The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. Conclusions The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

The QASC intervention required use of all three protocols. However, less than half downloaded them all and implementation was not guided by the proven implementation strategy. While encouraging that these resources were being used to drive practice change, piecemeal implementation of a proven intervention is unlikely to improve patient outcomes.

BACKGROUND Artificial intelligence (AI) holds the promise to support nurses’ clinical decision making in complex care situations or to conduct tasks that are remote from direct patient interaction such as documentation processes. There has been an increase in research and development of AI applications for nursing care, but a persistent lack of an extensive overview covering the evidence-base for promising application scenarios. OBJECTIVE The paper synthesizes literature on application scenarios for AI in nursing care settings, as well as highlighting adjacent aspects in the ethical, legal and social discourses surrounding the application of AI in nursing care. METHODS Following a rapid review design, databases PubMed, CINAHL, ACM Digital Library, IEEE Xplore, DBLP, and AIS Library, as well as the libraries of leading conferences were searched in June 2020. Publications of quantitative and qualitative original research, systematic reviews, or discussion papers and essays on ethical, legal, and social implications were eligible for inclusion. Based on predetermined selection criteria, eligible studies were analyzed. RESULTS Titles and abstracts of 6,818 publications and 699 fulltexts were screened and 285 publications have been included. Hospitals were the most prominent setting, followed by independent living-at-home, whereas less application scenarios for nursing homes or homecare were identified. Most studies employed machine learning algorithms while expert or hybrid systems were entailed in less than every tenth publication. Application context focused on image and signal processing with tracking, monitoring or classification of activity and health followed by care coordination and communication as well as fall detection was the main purpose of AI applications. Few studies reported effects for clinical or organizational outcomes of AI applications, lacking particularly in data gathered outside of laboratory conditions. Aside from technological requirements, reporting on requirements captures more overarching topics such as data privacy, safety or technology acceptance. Ethical, legal and social implications reflected the discourse on technology use in health care, but have gone mostly undiscussed in detail. CONCLUSIONS The results highlight potential for the application of AI systems in different care settings. With regard to the lack of findings on effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care specific perspective on objectives, outcomes and benefits. We find an advancement in the technological-societal discourse, surrounding the ethical and legal implications of AI applications in nursing care, to be a practical and needed next step for similar research groups. Further, we outline the need for a greater participation among stakeholders. CLINICALTRIAL not applicable

Zusammenfassung. Aufgrund komplexer Anforderungen und vielfältiger Barrieren scheitert es häufig, Wissen in die Breite zu tragen und umzusetzen. Pädagogik leistet hier große Arbeit: Sie lehrt, dass neues Wissen wichtig ist und zeigt Quellen sowie Bewertungskriterien. Der vorliegende Beitrag diskutiert die Rolle verschiedener Lösungen einschließlich Dominiks Pflegeuniversum zur Wissenszirkulation. Es zeigt sich, dass ein niedrigschwelliges Angebot zur Wissensverbreitung notwendig ist, was dort ansetzt, wo sich Pflegende aufhalten und austauschen – z. B. in sozialen Netzwerken.