A scoping review of deep learning in cancer nursing combined with augmented reality: The era of intelligent nursing is coming

Ren, Yulan; Yang, Yao; Chen, Jiani; Zhou, Ying; Li, Jiamei; Xia, Rui; Yang, Yuan; Wang, Qiao; Su, Xi

doi:10.1016/j.apjon.2022.100135

Cited by 11 publications

(10 citation statements)

References 49 publications

(130 reference statements)

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“…Implementation strategies involve collaborative efforts to align AI with nursing standards, integrate it into existing infrastructure, provide training, and ensure ethical use. These efforts promise to enhance patient care, streamline work ows, and address ethical, legal, and societal concerns, marking a signi cant advancement in nursing practice [38][39][40][41][42][43][44][45][46].…”

Section: Discussionmentioning

confidence: 99%

“…This holistic approach ensures adherence to guidelines and maximizes e ciency and convenience, leveraging the support of family members for an optimal caregiving experience [17]. The deep-learning prediction model and the resultant risk strati cation score may prove helpful in clinical decision-making under time-sensitive and resourceconstrained environments [38][39][40][41][42][43][44][45][46][47].…”

Section: Identify a Model Casementioning

confidence: 99%

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

Wangpitipanit,

Lininger,

Anderson

2024

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Identify a Model Casementioning

confidence: 99%

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

Wangpitipanit,

Lininger,

Anderson

2024

Preprint

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“…Ren et al, in [3], concentrated on CNN, ANN, and SVM in AR, explaining how AR and DL can be employed in healthcare. Unfortunately, this study had a limitation as it did not include all ML models.…”

Section: Related Workmentioning

confidence: 99%

“…AR has been enhanced by various technologies, including ML, which is still in its infancy. Despite this, it is used for various educational and training purposes, for example, medical education [3]. Following the periods of inference, knowledge, and ML, deep learning (DL) [4,5] represents the next phase in artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education

Khan,

Jamil,

Piran

et al. 2024

Technologies

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Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education.

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P‐2.17: The Research Status And Prospect of Augmented Reality in Medical Field

Yang

Hou

Wang

2023

Symp Digest of Tech Papers

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With the explosion of metaverse, augmented reality technology (AR) has been well developed. Augmented reality is a technology that connects the real and virtual worlds. In recent years, as the global population aging problem intensifies, the elderly are more dependent on advanced and guaranteed medical technology, AR+ medical has been further developed, and has received investment from many technology giants. AR technology has changed the form of traditional medical treatment. Through the study of AR+ medical treatment, the author analyzed the research results of AR in medical education, hand rehabilitation, medical plastic surgery, clinical medicine and other fields. The future research of AR in the medical field is prospected.

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A scoping review of deep learning in cancer nursing combined with augmented reality: The era of intelligent nursing is coming

Cited by 11 publications

References 49 publications

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

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

A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education

P‐2.17: The Research Status And Prospect of Augmented Reality in Medical Field

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