Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Integration of artificial intelligence into nursing practice
151
Zitationen
3
Autoren
2022
Jahr
Abstract
Background: Artificial Intelligence (AI) is developing at a rapid pace and finding new applications across the health service team. Some professionals have voiced concerns over the implementation of AI, whilst others predict greater job opportunities in the future. Nursing practice will be directly affected and further information is required on the knowledge and perceptions of nurses regarding the integration of AI in practice. The study aims to assess the knowledge, attitude, willingness, and organizational readiness in integrating AI into nursing practice. Methods: An exploratory cross-sectional survey of nurses working in health organisations. A survey link was emailed to participants. Nurses working in the United Arab Emirates (UAE) health organisations were invited to participate. Eligibility criteria included registered nurses in government or private hospitals. The survey captured the nurses demographic, knoweldage, preceptions, orgianizational readinesss and challenges regarding implementation of AI into nursing practice. Results: 553 responses were returned from 650 invitation giving a response rate of 85%. 51% of respondents stated their knowledge on AI was obtained through self-taught measures for most of the participants, while 20% of them gained it through various courses. Only 8% stated they learned through postgraduate courses, while 9% stated they lack knowledge of AI. 75% of all respondents agreed that the nursing curriculum should include some basic knowledge of AI. Conclusions: There is a lack of understanding of the principles of AI across the nursing profession. Further education and training is required to enable a seamless and safe integration of AI into nursing practice.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.687 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.591 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.114 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.867 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.