Exploring the Deep Learning of Artificial Intelligence in Nursing: A Concept Analysis with Walker and Avant’s Approach

Author:

Wangpitipanit Supichaya1,Lininger Jiraporn1,Anderson Nick2

Affiliation:

1. Mahidol University

2. University of California, Davis

Abstract

Abstract

Background: In recent years, increased attention has been given to the use ofartificial intelligence (AI) or artificial intelligence (AI) deep learning(DL) in healthcare to address nursing challenges. However, the adoption of new technologies in nursing needs to be improved, and AI in nursing is still in its early stages. However, the current literature needs more clarity, which affects clinical practice, research, and theory development. This study aimed to clarify the meaning of deep learning and identify the defining attributes of artificial intelligence within nursing. Methods: We conducted a concept analysis of the deep learning of AI in nursing care using Walker and Avant's 8-step approach. Our search strategy employed Boolean techniques across databases, including BMC, CINAHL, ClinicalKey for Nursing, Embase, Google Scholar, Ovid, Scopus, SpringerLink, ProQuest, PubMed, and Web of Science. By focusing on relevant keywords in titles and abstracts from articles published between 2018 and 2024, we initially found 574 sources. Results: Thirty-six articles that met the inclusion criteria were analyzed in this study. The attributes of evidence included four themes: focus and immersion, coding and understanding, arranging layers and algorithms, and implementing within the process of use cases to modify recommendations. Antecedents, unclear systems and communication, insufficient data management knowledge and support, and compound challenges can lead to suffering and risky caregiving tasks. The application of DL deep learning techniques enables nurses to simulate scenarios, predict outcomes, and plan care with greater precision. Embracing deep learning equipment allows nurses to makebetter decisions and empower them with enhanced knowledge, while ensuring adequate support and resources is essential for caregiver and patient well-being, and access to necessary equipment is vital for high-quality home healthcare. Conclusion: This study provides a clearer understanding of the use of deep learning in nursing and its implications for nursing practice. Future research should focus on exploring the impact of deep learning on healthcare operations management through quantitative and qualitative studies. Additionally, the development of a framework to guide the integration of deep learning into nursing practice is recommended to facilitate its adoption and implementation.

Publisher

Springer Science and Business Media LLC

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