Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant’s approach

The review of the literature emphasizes the critical importance of integrating AI into healthcare, particularly within nursing. Collaborative efforts between AI and nursing teams can potentially enhance healthcare outcomes, as the promotion of AI education among nursing staff is vital for sustainable development. These studies offer valuable insights into the role of deep learning in healthcare, potentially strengthening our understanding of deep learning in AI applications. Therefore, the main objective of this research is to explore deep learning in artificial intelligence within the context of nursing science through concept analysis.

This study explored how deep learning in artificial intelligence can be applied in nursing, aiming to define its framework and relevance within the field. This highlights the significance of integrating these concepts into nursing practice to enhance patient care using modern technological advancements. Through an analysis of relevant activities and projects in nursing science, this research aims to elucidate the fundamental principles and defining attributes of deep learning in AI.

An onomasiological approach was used to define the concept across various contexts. Deep learning involves the theory and development of computer systems capable of tasks like speech recognition, learning, visual perception, mathematical computations, reasoning, problem-solving, decision-making, and language translation. Unlike traditional algorithms that focus narrowly, deep learning employs supervised or unsupervised machine learning techniques. These methods create and refine neural network models through multi-layered data representations and nonlinear transformations. Medical Subject Headings (MeSH) terms vary in specificity in nursing research and literature. There isn’t a single MeSH term combining “deep learning” and “artificial intelligence” in nursing. However, terms like “Artificial Intelligence,” “Machine Learning,” and “Neural Networks” collectively cover these concepts. These terms help researchers access relevant studies in nursing databases, supporting advancements in patient care, clinical decision-making, and nursing education [25, 26].

Artificial intelligence was first coined by John McCarthy in 1956 at Dartmouth College. Since then, AI has evolved significantly, emphasizing technological foundations and cognitive resonance with human intelligence. This evolution led to machine learning paradigms introducing models like Artificial Neural Networks (ANNs) [1, 10‐12]. Deep learning, initially conceptualized by Dechter in 1986, constructs complex functions through layered neural networks. These architectures mimic hierarchical processing in biological brains with input, hidden, and output layers. Techniques like backpropagation refine mathematical operations to discern patterns and insights from extensive datasets [28]. This narrative begins with AI’s inception and traces its evolution to deep learning [1, 10‐12]. A detailed onomasiological analysis shows that “deep learning” involves immersion, focus, and profound understanding, contrasting with superficial notions. This cognitive engagement parallels human brain functions, supporting memory and learning pathways. In education and computer science, deep learning promotes critical thinking, problem-solving, and practical knowledge application [29, 30]. Projects and teamwork encourage deep subject exploration and creative solutions. Computationally, deep learning enhances performance through dataset analysis [31, 32]. This mirrors hierarchical brain learning with supervised, unsupervised, or reinforcement methods to absorb knowledge [9‐12, 33‐35].

Deep learning holds promise for transforming nursing practice by enhancing clinical decision-making, improving patient monitoring, and enabling predictive analytics. By leveraging these advanced technologies, nurses can provide more accurate, timely, and personalized care, ultimately improving patient outcomes and operational efficiency in healthcare settings. As the field evolves, ongoing research and collaboration between data scientists and healthcare professionals will be crucial to fully realizing the benefits of deep learning in nursing. In-depth learning refers to in-depth work that involves an input process and has functions in both analysis and algorithmic processing. It is characterized by its capacity to learn hierarchical data representations, progressing from fundamental concepts to more abstract ones. This hierarchical learning enables deep models to apprehend intricate patterns by capturing features at varying levels of abstraction. Training deep learning models necessitates extensive labeled datasets, commonly called big data, facilitating robust generalization to new examples and enhancing overall performance [9‐12]. The versatility of deep learning is evident in its wide-ranging applications across domains such as image and speech recognition and natural language processing, demonstrating its ability to perform diverse tasks. Specialized architectures such as ANNs can optimize performance by tailoring the model structures to handle specific data types and tasks effectively. Additionally, deep learning fosters autonomous knowledge acquisition, enabling machines to learn and apply knowledge from data without explicit programming autonomously. Compared with their shallow counterparts, deep networks often exhibit efficiency in approximating functions, achieving comparable results with fewer computational resources. As a subset of machine learning and artificial intelligence, deep learning aims to emulate human brain networks, enabling machines to learn and make decisions akin to human cognition. From a nursing science perspective, numerous studies have demonstrated the potential application of deep learning in research [9‐12, 30].

When delving into the intricacies of deep learning, specific components emerge. This entails synthesizing various works and organizing them into a structured framework harmonized with a formal control system to enhance nursing care. Focus, immerse, code, understand, arrange layers and algorithms, and implement within the use process. This involves (1) focusing and immersing individuals in the central point of interest or attention and thoroughly engaging individuals in an activity and implementing a tactile tool or a system that requires manual manipulation to initiate the process within a use case scenario—a series of actions to achieve a specific outcome. Focusing or visualizing the original knowledge, which in this context is deep learning, is crucial. Participants must be sincerely interested and immersed in the activity, aligning with predefined objectives or goals to foster commitment and engagement. This approach forms the cornerstone for success in any endeavor. (2) Coding and understanding are foundational pillars in the intricate landscape of deep learning, particularly in enhancing nursing care. In deep learning, coding involves meticulously arranging words, letters, or symbols to encapsulate messages, whether for encryption or concise communication. This process, which may utilize numerical, alphabetical, or symbolic systems, encodes information conducive to efficient comprehension and analysis. On the other hand, understanding delves into the profound assimilation and interpretation of conveyed meaning, facilitating informed decision-making. This comprehensive grasp of information is essential for nurses as they navigate the complexities of patient care, encompassing direct clinical interventions and the broader implications of illness and healthcare practices. Through deep understanding, nurses can adeptly decipher the intricacies of patient conditions, treatment plans, and the ever-evolving healthcare landscape, ultimately fostering optimal patient outcomes and enhancing healthcare delivery. (3) Arranging layers and algorithms involves structuring data akin to distinct material or thin sheet levels and utilizing mathematical instructions or rules to guide problem-solving. This process encompasses organizing layers based on specific instructions or mathematical principles, allowing computers to comprehend and process information effectively. This attribute also entails utilizing tools to execute tasks based on predefined instructions and algorithms. For instance, learning algorithms adeptly capture constraints involving a limited number of variables while arranging layers, and following prescribed algorithms ensures systematic processing of information to address the given problem. Finally, implementation involves employing tools following predefined instructions to achieve results aligned with the established algorithm. (4) In the implementation process within the use case, meticulous attention is directed toward practice, employing a systematic approach reminiscent of principles to yield favorable outcomes. This approach emphasizes the importance of evidence-based practices, drawing upon empirical evidence to enhance the quality of care and ultimately improve patient outcomes. Implementation strategies may involve the utilization of simulations or carefully crafted designs aimed at gaining a comprehensive understanding of patient needs and intricacies, facilitating the effective planning and delivery of care. By adhering to evidence-based practices and leveraging innovative tools such as simulations, healthcare professionals can navigate the complexities of patient care with precision and compassion, ensuring optimal outcomes and patient satisfaction. However, with the advancement of AI technology, the intricacies of utilizing deep learning in nursing have become increasingly significant. Investigating the role of AI is crucial, particularly in nurse registration. This approach involves developing a process to monitor advancements, encompassing health trends and nursing research [9‐12, 21, 31].

The idea of understanding deep learning can be divided into four parts: focusing, understanding code, organizing layers and algorithms, and applying it in real-life situations. Now, picture a community health nurse who plans a home visit (home health care [32, 36]) of a 98-year-old grandfather unable to leave his bed. The nurse assisted with daily tasks using a scale called the Barthel ADL Index, where a score of 1 out of 20 is typical. Additionally, family members, such as grandchildren-in-law, are waiting to assist, perhaps with tasks such as changing diapers twice a day. Imagine a community health nurse preparing for a home visit to care for a 98-year-old grandfather who is bedridden. Nurses rely on tools such as the Barthel ADL Index to assess the grandfather’s needs, which are typically just 1 out of 20. Family members, such as grandchildren-in-law, are also involved in caregiving, aiding with tasks such as changing diapers and scheduling visits. However, nurses face the challenge of understanding grandfathers’ complex health issues to provide tailored assistance effectively. To address this, the nurse delves into the patient’s records, utilizing electronic health records (EHR) systems to access crucial information for diagnosis and treatment. Mastery of medical applications and computer programs is also essential for medication management and accurate interpretation of test results. Additionally, nurses embrace telehealth tools, such as video calls, to facilitate efficient communication with healthcare teams, overcoming geographical barriers. However, ensuring patient privacy and data security remains paramount when utilizing these technologies. Organizing care plans and applying algorithms to meet the grandfather’s specific needs are crucial in caregiving. Understanding these principles allows nurses to provide systematic and efficient care while safeguarding patient information from unauthorized access or tampering. In essence, by integrating deep learning concepts into caregiving practices, nurses can increase the standard of home visits for older patients, such as grandfathers. This holistic approach ensures adherence to guidelines and maximizes efficiency and convenience, leveraging the support of family members for an optimal caregiving experience [17]. The deep learning prediction model and the resulting risk stratification score may prove helpful in clinical decision-making under time-sensitive and resource-constrained environments [37‐44].

The visiting nurse received an urgent call from a concerned patient, a 67-year-old individual seeking assistance from their 85-year-old grandmother who had recently suffered a debilitating stroke three months prior. The grandmother was bedridden, her movements were restricted, and her ability to sense her surroundings greatly diminished. Complicating matters, the feeding tube she relied upon often became dislodged, requiring frequent readjustments and careful monitoring. Upon arrival, the nurse encountered a distressing scene. Essential equipment needed to be included or completed, forcing the nurse to improvise with makeshift solutions. This hindered the efficiency of care and posed risks to the grandmother’s well-being. Adding to the situation’s complexity was the psychological and economic stress experienced by the family caregivers. Individuals constantly tending to meet the needs of elderly loved ones with serious health issues exacted a heavy toll, both emotionally and financially. The demands of continuous care left caregivers exhausted and overwhelmed, exacerbating the challenges faced by nurses upon arrival. Furthermore, the need for premade equipment added another layer of complications. Procuring these items often involved lengthy delays, further impeding the provision of timely and effective care—the strain of navigating these logistical hurdles not only exacerbated the already tense atmosphere. During these challenges, it became evident that the standard of care fell short of what was necessary for the grandmother’s well-being. Despite the caregivers’ best efforts, the lack of adequate preparation and support left the grandmother vulnerable and in need of more comprehensive care. This scenario underscores the critical importance of recognizing and addressing the psychological and economic stress experienced by family caregivers. Providing patients with adequate support and resources is essential for ensuring the well-being of both the caregiver and the care recipient. Additionally, ensuring that healthcare professionals access the necessary equipment and resources is vital for delivering high-quality care in home healthcare settings.

When exploring the antecedents of DL in nursing, it becomes evident that nurses across various care settings face overwhelming challenges and demands that directly impact patient care. These antecedents include the repetitive nature of caregiving tasks, inadequate knowledge of data management, and insufficient support. Nurses in both care facilities and community settings juggle multiple roles amidst ambiguity in systems and communication, leading to risky and repetitive caregiving tasks that contribute to heightened stress and fatigue. The lack of knowledge in data management significantly hinders the effective utilization of the nursing process, while insufficient support and resources exacerbate these challenges, impeding effective communication and continuous learning. This intensifies feelings of isolation and frustration among caregivers, underscoring the complexities inherent in caregiving and highlighting the need for enhanced support and training in data management and communication [17, 18]. Consequently, integrating DL in nursing can lead to several beneficial outcomes. Enhanced support and training foster trust and precision in evaluating and planning nursing care, resulting in a more explicit, outcome-oriented process. Nurses can seamlessly apply DL technology within their practice, intertwining it with various related arts such as simulating complex scenarios, crafting intricate decision trees, and predicting outcomes through advanced algorithms like deep belief networks and multilayer perceptron’s. This integration addresses existing challenges and advances the profession by enhancing the accuracy and efficiency of patient care, ultimately leading to better patient outcomes and a more efficient healthcare system [17, 18].

Directly connected to the theory behind DL, we see that whatever can be measured is retained. This is evident when we try receiving and interpreting signals. In nursing, careful attention is given to science. We collected various data for analysis, aiming for meaningful results. Understanding these principles empowers nurses to provide well-organized, impactful data-based care. It has been noted that what can be measured there is kept. This is noticeable in the trial of the reception of the receipt signal. Care in the scientific direction of nursing may be all forms of data recorded to be analyzed and interpreted appropriately, and possible results may be obtained. Understanding these principles helps nurses provide systematic, effective care based on data.