Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant’s approach
Issued Date
2024-12-01
Resource Type
eISSN
14726955
Scopus ID
2-s2.0-85200233862
Journal Title
BMC Nursing
Volume
23
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMC Nursing Vol.23 No.1 (2024)
Suggested Citation
Wangpitipanit S., Lininger J., Anderson N. Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant’s approach. BMC Nursing Vol.23 No.1 (2024). doi:10.1186/s12912-024-02170-x Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/100400
Title
Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant’s approach
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Author's Affiliation
Corresponding Author(s)
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Abstract
Background: In recent years, increased attention has been given to using deep learning (DL) of artificial intelligence (AI) in healthcare to address nursing challenges. 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 and MeSH terms across databases, including BMC, CINAHL, ClinicalKey for Nursing, Embase, Ovid, Scopus, SpringerLink and Spinger Nature, 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 571 sources. Results: Thirty-seven 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. Applying deep learning techniques enables nurses to simulate scenarios, predict outcomes, and plan care more precisely. Embracing deep learning equipment allows nurses to make better decisions. It empowers them with enhanced knowledge while ensuring adequate support and resources essential for caregiver and patient well-being. 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, developing a framework to guide the integration of deep learning into nursing practice is recommended to facilitate its adoption and implementation.