ArticleBMC nursing2024
Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant's approach.
Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Review
- Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients.Nursing in critical care · 2026Article
- Nursing Educators' Perceptions of AI in Research: Risks and Benefits.Nursing research and practice · 2026Article
- Advancing Nursing Through Artificial Intelligence: A Systematic Literature Review of Current Evidence.Cureus · 2025Review
- Navigating artificial intelligence in home healthcare: challenges and opportunities in nursing wound care.BMC nursing · 2025Article
- Neonatal nurses' experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus.BMC nursing · 2025Article
- Ethical Considerations in the Use of Artificial Intelligence in Pain Medicine.Current pain and headache reports · 2025Review
- Artificial Intelligence and Nursing Management: Opportunities, Challenges, and Ethical Considerations-A Scoping Review.Journal of nursing management · 2025Article
- Examining patient safety protocols amidst the rise of digital health and telemedicine: nurses' perspectives.BMC nursing · 2024Article
- Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses' perspectives.BMC nursing · 2024Article
- Nursing Educators' Perspectives on the Integration of Artificial Intelligence Into Academic Settings.SAGE open nursingArticle
- Artificial Intelligence in Nursing Governance and Regulation: An Umbrella Review of Ethical and Policy Dimensions.SAGE open nursingReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
methodsWe 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.
resultsThirty-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.
conclusionThis 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.