Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

Role of artificial intelligence in early identification and risk evaluation of non-communicable diseases: a bibliometric analysis of global research trends

OBJECTIVE

This study aims to shed light on the transformative potential of artificial intelligence (AI) in the early detection and risk assessment of non-communicable diseases (NCDs).

STUDY DESIGN

Bibliometric analysis.

SETTING

Articles related to AI in early identification and risk evaluation of NCDs from 2000 to 2024 were retrieved from the Scopus database.

METHODS

This comprehensive bibliometric study focuses on a single database, Scopus and employs narrative synthesis for concise yet informative summaries. Microsoft Excel V.365 and VOSviewer software (V.1.6.20) were used to summarise bibliometric features.

RESULTS

The study retrieved 1745 relevant articles, with a notable surge in research activity in recent years. Core journals included Scientific Reports and IEEE Access, and core institutions included the Harvard Medical School and the Ministry of Education of the People's Republic of China, while core countries comprised China, the USA, India, the UK and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI's impact on NCDs management. Frequent author keywords identified key research hotspots, including specific NCDs like Alzheimer's disease and diabetes. Risk assessment studies demonstrated improved predictions for heart failure, cardiovascular risk, breast cancer, diabetes and inflammatory bowel disease.

CONCLUSION

Our findings highlight the increasing role of AI in early detection and risk prediction of NCDs, emphasising its widening research impact and future clinical potential.

Harnessing machine learning for predicting successful weaning from mechanical ventilation: A systematic review

BACKGROUND

Machine learning (ML) models represent advanced computational approaches with increasing application in predicting successful weaning from mechanical ventilation (MV). Whilst ML itself has a long history, its application to MV weaning outcomes has emerged more recently. In this systematic review, we assessed the effects of ML on the prediction of successful weaning outcomes amongst adult patients undergoing MV.

METHODS

PubMed, EMBASE, Scopus, Web of Science, and Google Scholar electronic databases were searched up to May 2024. In addition, ACM Digital Library and IEEE Xplore databases were searched. We included peer-reviewed studies examining ML models for the prediction of successful MV in adult patients. We used a modified version of the Joanna Briggs Institute checklist for quality assessment.

RESULTS

Eleven studies (n = 18 336) were included. Boosting algorithms, including extreme gradient boosting (XGBoost) and Light Gradient-Boosting Machine, were amongst the most frequently used methods, followed by random forest, multilayer perceptron, logistic regression, artificial neural networks, and convolutional neural networks, a deep learning model. The most common cross-validation methods included five-fold and 10-fold cross-validation. Model performance varied, with the artificial neural network accuracy ranging from 77% to 80%, multilayer perceptron achieving 87% accuracy and 94% precision, and convolutional neural network showing areas under the curve of 91% and 94%. XGBoost generally outperformed other models in the area under the curve comparisons. Quality assessment indicated that almost all studies had high quality as seven out of 10 studies had full scores.

CONCLUSIONS

ML models effectively predicted weaning outcomes in adult patients undergoing MV, with XGBoost outperforming other models. However, the absence of studies utilising newer architectures, such as transformer models, highlights an opportunity for further exploration and refinement in this field.

Advancements and innovations in liquid biopsy through microfluidic technology for cancer diagnosis

Cancer is one of the leading causes of death worldwide, with approximately 10 million deaths and almost 20 million cases diagnosed in 2022. Various diagnostic methods for cancer, including physical examination, lab tests, imaging, and biopsy (tissue or liquid), are available in clinical settings. Liquid biopsy earned considerable attention due to its minimal invasiveness, patient convenience, and rapidness. Liquid biopsy is experiencing a significant transformation owing to the incorporation of microfluidic technologies. Microfluidic technologies allow for real-time observations and precise, sensitive, and efficient results in early cancer diagnosis through the identification of various biomarkers using body fluids at the microscale. This review highlights the transition from conventional cancer diagnostic methods to critically analyzing innovations and the integration of modern microfluidic technologies, presenting their influence in improving cancer diagnosis. This review highlights the significance of identifying exosomes and their biological components, such as micro RNAs, circular RNA, and mRNA, via microfluidics as biomarkers for cancer diagnosis. It also highlights the integration of microfluidics with advanced technologies, such as CRISPR gene editing, organ-on-a-chip models, 3D bioprinting, and nanotechnology, for cancer diagnosis. Moreover, integrating artificial intelligence into microfluidic systems has significantly transformed research related to cancer diagnosis. This advancement enables more precise diagnosis and personalized treatment strategies using the large available data on networks along with algorithmic approaches. Collectively, microfluidics and their integration into advanced technologies have shown the potential for progress in early cancer diagnosis and the customization of treatment approaches, such as immunotherapy, in the future.

Artificial intelligence: its potential in personalized public health strategies and genetic data analysis: a narrative review

This review comprehensively evaluates personalized public health strategies using artificial intelligence (AI) in disease prediction/management and genetic data analysis. In the field of healthcare, AI has achieved significant advancements in the analysis of public health and genetic data. Its applications in public health include predicting the spread of infectious diseases, evaluating individual risk factors, and optimizing resource management. In the realm of genetic data, AI offers groundbreaking contributions such as identifying disease risk factors, analyzing genetic mutations, and developing personalized treatment plans. In this review, we evaluated the importance of AI in preventive medicine in a structured way and by including concrete application examples. Ethical and legal responsibilities must be considered due to the significant implications of AI-generated decisions. By integrating AI into public health and genetics, we are poised to unlock unprecedented opportunities for advancing human health. This approach not only enhances our ability to understand and address complex health challenges but also paves the way for equitable, effective, and individualized care solutions on a global scale. In this review, we addressed to the interactions between particular subdomains of personalized public health strategies and AI with most recent literature and legal/ethical perspective.

Neonatal nurses' experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus

BACKGROUND

Neonatal nurses in high-risk Neonatal Intensive Care Units (NICUs) navigate complex, time-sensitive clinical decisions where accuracy and judgment are critical. Generative artificial intelligence (AI) has emerged as a supportive tool, yet its integration raises concerns about its impact on nurses' decision-making, professional autonomy, and organizational workflows.

AIM

This study explored how neonatal nurses experience and integrate generative AI in clinical decision-making, examining its influence on nursing practice, organizational dynamics, and cultural adaptation in Saudi Arabian NICUs.

METHODS

An interpretive phenomenological approach, guided by Complexity Science, Normalization Process Theory, and Tanner's Clinical Judgment Model, was employed. A purposive sample of 33 neonatal nurses participated in semi-structured interviews and focus groups. Thematic analysis was used to code and interpret data, supported by an inter-rater reliability of 0.88. Simple frequency counts were included to illustrate the prevalence of themes but were not used as quantitative measures. Trustworthiness was ensured through reflexive journaling, peer debriefing, and member checking.

RESULTS

Five themes emerged: (1) Clinical Decision-Making, where 93.9% of nurses reported that AI-enhanced judgment but required human validation; (2) Professional Practice Transformation, with 84.8% noting evolving role boundaries and workflow changes; (3) Organizational Factors, as 97.0% emphasized the necessity of infrastructure, training, and policy integration; (4) Cultural Influences, with 87.9% highlighting AI's alignment with family-centered care; and (5) Implementation Challenges, where 90.9% identified technical barriers and adaptation strategies.

CONCLUSIONS

Generative AI can support neonatal nurses in clinical decision-making, but its effectiveness depends on structured training, reliable infrastructure, and culturally sensitive implementation. These findings provide evidence-based insights for policymakers and healthcare leaders to ensure AI integration enhances nursing expertise while maintaining safe, patient-centered care.

The Role of AI in Nursing Education and Practice: Umbrella Review

BACKGROUND

Artificial intelligence (AI) is rapidly transforming health care, offering substantial advancements in patient care, clinical workflows, and nursing education.

OBJECTIVE

This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing.

METHODS

We included systematic reviews, scoping reviews, rapid reviews, narrative reviews, literature reviews, and meta-analyses focusing on AI integration in nursing, published up to October 2024. A new search was conducted in January 2025 to identify any potentially eligible reviews published thereafter. However, no new reviews were found. Eligibility was guided by the Sample, Phenomenon of Interest, Design, Evaluation, Research type framework; databases (PubMed or MEDLINE, CINAHL, Web of Science, Embase, and IEEE Xplore) were searched using comprehensive keywords. Two reviewers independently screened records and extracted data. Risk of bias was assessed with Risk of Bias in Systematic Reviews (ROBIS) and A Measurement Tool to Assess Systematic Reviews, version 2 (AMSTAR 2), which we adapted for systematic and nonsystematic review types. A thematic synthesis approach, conducted independently by 2 reviewers, identified recurring patterns across the included reviews.

RESULTS

The search strategy yielded 18 eligible studies after screening 274 records. These studies encompassed diverse methodologies and focused on nursing professionals, students, educators, and researchers. First, ethical and social implications were consistently highlighted, with studies emphasizing concerns about data privacy, algorithmic bias, transparency, accountability, and the necessity for equitable access to AI technologies. Second, the transformation of nursing education emerged as a critical area, with an urgent need to update curricula by integrating AI-driven educational tools and fostering both technical competencies and ethical decision-making skills among nursing students and professionals. Third, strategies for integration were identified as essential for effective implementation, calling for scalable models, robust ethical frameworks, and interdisciplinary collaboration, while also addressing key barriers such as resistance to AI adoption, lack of standardized AI education, and disparities in technology access.

CONCLUSIONS

AI holds substantial promises for revolutionizing nursing practice and education. However, realizing this potential necessitates a strategic approach that addresses ethical concerns, integrates AI literacy into nursing curricula, and ensures equitable access to AI technologies. Limitations of this review include the heterogeneity of included studies and potential publication bias. Our findings underscore the need for comprehensive ethical frameworks and regulatory guidelines tailored to nursing applications, updated nursing curricula to include AI literacy and ethical training, and investments in infrastructure to promote equitable AI access. Future research should focus on developing standardized implementation strategies and evaluating the long-term impacts of AI integration on nursing practice and patient outcomes.

The Role of Artificial Intelligence in Nursing Care: An Umbrella Review

Artificial intelligence (AI) is revolutionizing nursing by enhancing decision-making, patient monitoring, and efficiency. Machine learning, natural language processing (NLP), and predictive analytics claim to improve safety and automate tasks. However, a structured analysis of AI applications is necessary to ensure their effective implementation in nursing practice. This umbrella review aimed to synthesize existing systematic reviews on AI applications in nursing care, providing a comprehensive analysis of its benefits, challenges, and ethical implications. By consolidating findings from multiple sources, this review seeks to offer evidence-based insights to guide the effective and responsible integration of AI in nursing practice. A systematic umbrella review approach was employed following PRISMA guidelines. Multiple databases, including PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore, were searched for review articles published between 2015 and 2024. Findings were synthesized thematically to identify key trends, benefits, limitations, and research gaps. This review synthesized 13 studies, emphasizing AI's impact on clinical decision support, patient monitoring, nursing education, and workflow optimization. AI enhances early disease detection, minimizes diagnostic errors, and automates documentation, improving efficiency. However, data privacy risks, biases, ethical concerns, and limited AI literacy hinder integration. AI presents significant opportunities for improving nursing care, yet its successful implementation requires addressing ethical, legal, and practical challenges. Adequate AI training, robust data governance frameworks, and policies ensuring responsible AI use are essential for its integration into nursing practice. Future research should explore long-term AI impact, training models for nurses, and strategies to balance AI-driven efficiency with human-centered care.

Artificial intelligence in nursing: an integrative review of clinical and operational impacts

Background

Advances in digital technologies and artificial intelligence (AI) are reshaping healthcare delivery, with AI increasingly integrated into nursing practice. These innovations promise enhanced diagnostic precision, improved operational workflows, and more personalized patient care. However, the direct impact of AI on clinical outcomes, workflow efficiency, and nursing staff well-being requires further elucidation.

Methods

This integrative review synthesized findings from 18 studies published through November 2024 across diverse healthcare settings. Using the PRISMA 2020 and SPIDER frameworks alongside rigorous quality appraisal tools (MMAT and ROBINS-I), the review examined the multifaceted effects of AI integration in nursing. Our analysis focused on three principal domains: clinical advancements and patient monitoring, operational efficiency and workload management, and ethical implications.

Results

The review demonstrates that AI integration in nursing has yielded substantial clinical and operational benefits. AI-powered monitoring systems, including wearable sensors and real-time alert platforms, have enabled nurses to detect subtle physiological changes-such as early fever onset or pain indicators-well before traditional methods, resulting in timely interventions that reduce complications, shorten hospital stays, and lower readmission rates. For example, several studies reported that early-warning algorithms facilitated faster clinical responses, thereby improving patient safety and outcomes. Operationally, AI-based automation of routine tasks (e.g., scheduling, administrative documentation, and predictive workload classification) has streamlined resource allocation. These efficiencies have led to a measurable reduction in nurse burnout and improved job satisfaction, as nurses can devote more time to direct patient care. However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. These issues underscore the need for robust ethical frameworks and targeted AI literacy training within nursing curricula.

Conclusion

This review demonstrates that AI integration holds transformative potential for nursing practice by enhancing both clinical outcomes and operational efficiency. However, to realize these benefits fully, it is imperative to develop robust ethical frameworks, incorporate comprehensive AI literacy training into nursing education, and foster interdisciplinary collaboration. Future longitudinal studies across varied clinical contexts are essential to validate these findings and support the sustainable, equitable implementation of AI technologies in nursing. Policymakers and healthcare leaders must prioritize investments in AI solutions that complement the expertise of nursing professionals while addressing ethical risks.

Nurse retention in peri- and post-COVID-19 work environments: a scoping review of factors, strategies and interventions

OBJECTIVES

The COVID-19 pandemic highlighted the deterioration of nurses' working conditions and a growing global nursing shortage. Little is known about the factors, strategies and interventions that could improve nurse retention in the peri- and post-COVID-19 period. An improved understanding of strategies that support and retain nurses will provide a foundation for developing informed approaches to sustaining the nursing workforce. The aim of this scoping review is to investigate and describe the (1) factors associated with nurse retention, (2) strategies to support nurse retention and (3) interventions that have been tested to support nurse retention, during and after the COVID-19 pandemic.

DESIGN

Scoping review.

DATA SOURCES

This scoping review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. MEDLINE, Embase, CINAHL and Scopus databases were searched on 17 April 2024. The search was limited to a publication date of '2019 to present'.

ELIGIBILITY CRITERIA

Qualitative, quantitative, mixed-methods and grey literature studies of nurses (Registered Nurse (RN), Licenced Practical Nurse (LPN), Registered Practical Nurse (RPN), Publlic Health Nurse (PHN), including factors, strategies and/or interventions to support nurse retention in the peri- and post-COVID-19 period in English (or translated into English), were included. Systematic reviews, scoping reviews and meta-syntheses were excluded, but their reference lists were hand-screened for suitable studies.

DATA EXTRACTION AND SYNTHESIS

The following data items were extracted: title, journal, authors, year of publication, country of publication, setting, population (n=), factors that mitigate intent to leave (or other retention measure), strategies to address nurse retention, interventions that address nurse retention, tools that measure retention/turnover intention, retention rates and/or scores. Data were evaluated for quality and synthesised qualitatively to map the current available evidence.

RESULTS

Our search identified 130 studies for inclusion in the analysis. The majority measured some aspect of nurse retention. A number of factors were identified as impacting nurse retention including nurse demographics, safe staffing and work environments, psychological well-being and COVID-19-specific impacts. Nurse retention strategies included ensuring safe flexible staffing and quality work environments, enhancing organisational mental health and wellness supports, improved leadership and communication, more professional development and mentorship opportunities, and better compensation and incentives. Only nine interventions that address nurse retention were identified.

CONCLUSIONS

Given the importance of nurse retention for a variety of key outcomes, it is imperative that nursing leadership, healthcare organisations and governments work to develop and test interventions that address nurse retention.

The evolving role of nursing informatics in the era of artificial intelligence

AIM

This narrative review explores the integration of artificial intelligence (AI) into nursing informatics and examines its impact on nursing practice, healthcare delivery, education, and policy.

BACKGROUND

Nursing informatics, which merges nursing science with information management and communication technologies, is crucial in modern healthcare. The emergence of AI presents opportunities to improve diagnostics, treatment, and healthcare resource management. However, integrating AI into nursing practice also brings challenges, including ethical concerns and the need for specialized training.

SOURCES OF EVIDENCE

A comprehensive literature search was conducted from January 2013 to December 2023 using databases like PubMed, Google Scholar, and Scopus. Articles were selected based on their relevance to AI's role in nursing informatics, particularly in enhancing patient care and healthcare efficiency.

DISCUSSION

AI significantly enhances nursing practice by improving diagnostic accuracy, optimizing care plans, and supporting resource allocation. However, its adoption raises ethical issues, such as data privacy concerns and biases within AI algorithms. Ensuring that nurses are adequately trained in AI technologies is essential for safe and effective integration.

IMPLICATIONS FOR NURSING PRACTICE AND POLICY

Policymakers should promote AI literacy programs for healthcare professionals and develop ethical guidelines to govern the use of AI in healthcare. This will ensure that AI tools are implemented responsibly, protecting patient rights and enhancing healthcare outcomes.

CONCLUSION

AI offers promising advancements in nursing informatics, leading to more efficient patient care and improved decision-making. Nonetheless, overcoming ethical challenges and ensuring AI literacy among nurses are critical steps for successful implementation.

Attitudes of nurses toward artificial intelligence: A multicenter comparison

BackgroundArtificial intelligence (AI) is transforming medical practices with rapidly developing technologies and the innovative solutions it provides. In order for this transformation to be successfully integrated into healthcare services, healthcare professionals must have positive attitudes towards this technology.ObjectiveThe present study was conducted with the aim of comparing the attitudes of nurses working in different provinces towards artificial intelligence.MethodsThe study was planned in a descriptive cross-sectional design. The study population consisted of 1453 nurses working in 3 state hospitals (inpatient hospitals providing secondary health care services) located in the city centers of Muş, Bingöl and Adıyaman provinces in eastern Turkey. While the sample size was 698 nurses in total, the study was completed with 737 nurses. The data were collected through the Introductory Information Form and the General Attitudes toward Artificial Intelligence Scale (GAAIS). ANOVA test and multiple regression were used to analyse the data.ResultsIt was found that the nurses had highly positive attitudes towards artificial intelligence. When the nurses' scores from the Positive GAAIS sub-dimension were compared, it was determined that there was a significant difference (p < 0.05) between the provinces. A statistically significant difference (p < 0.01) was found between the provinces in the Negative GAAIS sub-dimension, as well. Demographic characteristics were found to be effective on both Positive GAAIS and Negative GAAIS.ConclusionsAlthough there were differences between the provinces, the nurses generally had positive attitudes towards artificial intelligence technologies. The majority of the participants continue to use artificial intelligence technologies although they state that artificial intelligence will replace humans in the future. Longitudinal studies on the factors affecting attitudes towards artificial intelligence are recommended.

Paediatric nurses' perspectives on artificial intelligence applications: A cross-sectional study of concerns, literacy levels and attitudes

AIMS

This study aimed to explore the correlation between artificial intelligence (AI) literacy, AI anxiety and AI attitudes among paediatric nurses, as well as identify the influencing factors on paediatric nurses' AI attitudes.

DESIGN

A descriptive, correlational and cross-sectional research.

METHODS

This study was conducted between January and February 2024 with 170 nurses actively working in paediatric clinics in Turkey. The data collection tools included the Nurse Information Form, the General Attitudes Towards Artificial Intelligence Scale (GAAIS), the Artificial Intelligence Literacy Scale (AILS) and the Artificial Intelligence Anxiety Scale (AIAS). To determine the associations between the variables, the data was analysed using IBM SPSS 28, which included linear regression and Pearson correlation analysis.

RESULTS

The study indicated significant positive correlations between paediatric nurses' age and their AIAS scores (r = .226; p < .01) and significant negative correlations between paediatric nurses' age and their AILS (r = -.192; p < .05) and GAAIS scores (r = -.152; p < .05). The GAAIS was significantly predictive (p < .000) and accounted for 50% of the variation in AIAS and AILS scores.

CONCLUSION

Paediatric nurses' attitudes towards AI significantly predicted AI literacy and AI anxiety. The relationship between the age of the paediatric nurses and the anxiety, AI literacy and attitudes towards AI was demonstrated. Healthcare and educational institutions should create customized training programs and awareness-raising activities for older nurses, as there are noticeable variations in the attitudes of paediatric nurses towards AI based on their age.

IMPLICATIONS FOR PROFESSION AND/OR PATIENT CARE

Providing in-service AI training can help healthcare organizations improve paediatric nurses' attitudes towards AI, increase their AI literacy and reduce their anxiety. This training has the potential to impact their attitudes positively and reduce their anxiety.

REPORTING METHOD

The study results were critically reported using STROBE criteria.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Ethical and regulatory considerations in the use of AI and machine learning in nursing: A systematic review

AIM

This study systematically explores the ethical and regulatory considerations surrounding the integration of artificial intelligence (AI) and machine learning (ML) in nursing practice, with a focus on patient autonomy, data privacy, algorithmic bias, and accountability.

BACKGROUND

AI and ML are transforming nursing practice by enhancing clinical decision-making and operational efficiency. However, these technologies present significant ethical challenges related to ensuring patient autonomy, safeguarding data privacy, mitigating algorithmic bias, and ensuring transparency in decision-making processes. Current frameworks are not sufficiently tailored to nursing-specific contexts.

METHODS

A systematic review was conducted, adhering to PRISMA guidelines. Six major databases were searched for studies published between 2000 and 2024. Seventeen studies met the inclusion criteria and were included in the final analysis.

RESULTS

Five key themes emerged from the review: enhancement of clinical decision-making, promotion of ethical awareness, support for routine nursing tasks, challenges in algorithmic bias, and the importance of public engagement in regulatory frameworks. The review identified critical gaps in nursing-specific ethical guidelines and regulatory oversight for AI integration in practice.

DISCUSSION

AI technologies offer substantial benefits for nursing, particularly in decision-making and task efficiency. However, these advantages must be balanced against ethical concerns, including the protection of patient rights, algorithmic transparency, and bias mitigation. Current regulatory frameworks require adaptation to meet the ethical needs of nursing.

CONCLUSION AND IMPLICATIONS FOR NURSING AND HEALTH POLICY

The findings emphasize the need for the development of nursing-specific ethical guidelines and robust regulatory frameworks to ensure the responsible integration of AI technologies into nursing practice. AI integration must uphold ethical principles while enhancing the quality of care.

Perceptions and Attitudes of Registered Nurses and Nursing Students Toward Advanced Technology and Artificial Intelligence: A Review of Literature

The use of technology in healthcare and healthcare education settings has increased rapidly across the United States and accelerated due to the COVID-19 pandemic. However, perceptions of new technologies in clinical nursing and nursing education are not well understood. Yet, understanding perceptions of registered nurses and nursing students toward advanced technology and artificial intelligence in clinical care and education is crucial if we are to implement these care delivery and educational innovations. This literature review investigates existing literature on registered nurses' and nursing students' attitudes toward advanced technology and artificial intelligence in nursing, including nursing education. Ten peer-reviewed studies published between 2017 and 2022 were reviewed. Findings revealed positive perceptions, such as improved patient care, efficiency, and reduced human error, but also concerns about job displacement, loss of human touch, and ethical/legal issues. Challenges in implementation, adequate training in technologies, and how technologies may reduce the human connection aspect of nursing care were identified. By recognizing the attitudes and perceptions of registered nurses and nursing students toward these advanced technologies, we can better ensure that it is ethically, effectively, and responsibly integrated into nursing practice and education.

Examining the Role of AI in Changing the Role of Nurses in Patient Care: Systematic Review

BACKGROUND

This review investigates the relationship between artificial intelligence (AI) use and the role of nurses in patient care. AI exists in health care for clinical decision support, disease management, patient engagement, and operational improvement and will continue to grow in popularity, especially in the nursing field.

OBJECTIVE

We aim to examine whether AI integration into nursing practice may have led to a change in the role of nurses in patient care.

METHODS

To compile pertinent data on AI and nursing and their relationship, we conducted a thorough systematic review literature analysis using secondary data sources, including academic literature from the Scopus database, industry reports, and government publications. A total of 401 resources were reviewed, and 53 sources were ultimately included in the paper, comprising 50 peer-reviewed journal articles, 1 conference proceeding, and 2 reports. To categorize and find patterns in the data, we used thematic analysis to categorize the systematic literature review findings into 3 primary themes and 9 secondary themes. To demonstrate whether a role change existed or was forecasted to exist, case studies of AI applications and examples were also relied on.

RESULTS

The research shows that all health care practitioners will be impacted by the revolutionary technology known as AI. Nurses should be at the forefront of this technology and be empowered throughout the implementation process of any of its tools that may accelerate innovation, improve decision-making, automate and speed up processes, and save overall costs in nursing practice.

CONCLUSIONS

This study adds to the existing body of knowledge about the applications of AI in nursing and its consequences in changing the role of nurses in patient care. To further investigate the connection between AI and the role of nurses in patient care, future studies can use quantitative techniques based on recruiting nurses who have been involved in AI tool deployment-whether from a design aspect or operational use-and gathering empirical data for that purpose.

Ethical implications of artificial intelligence integration in nursing practice in arab countries: literature review

BACKGROUND

Applying artificial intelligence (AI) to nursing practice has dramatically enhanced healthcare delivery in Arab countries. However, AI application also raises complex moral issues, including patient privacy, data security, responsibility, transparency, and equity in decision-making.

AIM

A systematic analysis of the ethical issues surrounding the application of AI in nursing practice in Arab nations is carried out in this review, highlighting the most important ethical issues and recommending responsible AI integration.

METHODS

A comprehensive literature search was conducted across major databases. Following the initial identification of 150 articles, 120 were selected for full-text review based on the title and abstract screening. Subsequently, 50 pertinent studies were incorporated into this review.

RESULTS

Numerous significant ethical concerns regarding AI application in decision-making processes were identified. The assessment also highlighted the possible effects of AI on the nurse-patient interaction and the critical role played by the ethics committees and regulatory frameworks in resolving these issues.

CONCLUSION

Ethical frameworks must be established to guarantee AI integration into nursing practice, safeguard patients' welfare, and strengthen the trust between healthcare providers and patients.

CLINICAL TRIAL

No clinical Trial.

Artificially intelligent nursing homes: a scoping review of palliative care interventions

Introduction

The world's population is aging at a rapid rate. Nursing homes are needed to care for an increasing number of older adults. Palliative care can improve the quality of life of nursing home residents. Artificial Intelligence can be used to improve palliative care services. The aim of this scoping review is to synthesize research surrounding AI-based palliative care interventions in nursing homes.

Methods

A PRISMA-ScR scoping review was carried out using modified guidelines specifically designed for computer science research. A wide range of keywords are considered in searching six databases, including IEEE, ACM, and SpringerLink.

Results

We screened 3255 articles for inclusion after duplicate removal. 3175 articles were excluded during title and abstract screening. A further 61 articles were excluded during the full-text screening stage. We included 19 articles in our analysis. Studies either focus on intelligent physical systems or decision support systems. There is a clear divide between the two types of technologies. There are key issues to address in future research surrounding palliative definitions, data accessibility, and stakeholder involvement.

Discussion

This paper presents the first review to consolidate research on palliative care interventions in nursing homes. The findings of this review indicate that integrated intelligent physical systems and decision support systems have yet to be explored. A broad range of machine learning solutions remain unused within the context of nursing home palliative care. These findings are of relevance to both nurses and computer scientists, who may use this review to reflect on their own practices when developing such technology.

Psychometrics of the Attitude Scale towards the use of Artificial Intelligence Technologies in Nursing

BACKGROUND

It is clear that nursing practice is directly affected by artificial intelligence (AI), and in this regard, a need is felt for more information on the knowledge and attitudes of nurses to the use of AI technology in nursing care practice. However, no inclusive measurement instrument tested for validity and reliability evaluating the attitudes of nurses to the use of AI technology was found. The aim of this research was to develop and test the validity of the Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing (ASUAITIN) in the Turkish language.

METHODS

The research was a methodological and cross-sectional study, designed to develop and test the validity of the ASUAITIN. STROBE guidelines were followed in the study. In order to create the starting materials, the researchers made a scan of the literature. Two hundred nurses working in the internal medicine, surgical and intensive care departments of a university hospital in the Marmara Region of Turkey were included in the study. Items were assessed for content validity. ASUAITIN was tested for construct validity and internal consistency reliability.

RESULTS

ASUAITIN consists of 15 items. It has two dimensions, positive attitude, and negative attitude to AI technology in nursing practice, and practice and explains 67.762% of total variance. Item loads were between 0.529 and 0.866. Cronbach alpha values were calculated to be 0.910 for the total scale, 0.933 for Factor 1, and 0.917 for Factor 2.

CONCLUSIONS

The results of this study show that the ASUAITIN scale are validated and reliable measurement tool. ASUAITIN can be used as an instrument to assess the attitudes to AI technology in practice among nurses working in the clinical field.

CLINICAL TRIAL NUMBER

Not applicable.

The Role of Personality Traits in Nursing Students' Attitudes Toward Artificial Intelligence

AIM

 This study assesses nursing students' attitudes toward artificial intelligence (AI) and examines the role of personality traits in shaping these attitudes.  Methods: A cross-sectional study was conducted in which 159 nursing students from the University of Thessaly participated. Data were collected using the General Attitudes Toward Artificial Intelligence Scale (GAAIS) to measure attitudes toward AI and the Ten Item Personality Inventory (TIPI) to assess personality traits. Statistical analysis included descriptive and inferential methods, such as correlation and factor analysis. The significant level was set to p<0.05.  Results: The findings revealed moderately positive attitudes toward AI (mean positive attitude score: 3.22 out of 5). Extraversion and openness to experience were positively correlated with positive attitudes, while maternal education was significantly associated with lower negative attitudes.  Conclusion: Nursing students demonstrate a cautious optimism toward AI, with personality traits and education playing a key role in shaping their perceptions. Addressing concerns about AI through targeted educational programs could enhance students' confidence and willingness to adopt AI in their professional practice. These findings emphasize the importance of integrating AI into nursing curricula to bridge knowledge gaps and promote the effective use of AI technologies in healthcare.

A Cross-Sectional Online Study of the Use of Artificial Intelligence in Nursing Research as Perceived by Nursing Students

Background

The use of artificial intelligence (AI) in healthcare in general and scientific research in particular has become increasingly prevalent as it holds great promise for optimizing research processes and outcomes.

Aims

This study described predictors and differences in students' perceptions of the risks and benefits related to using AI in nursing research.

Methods

A quantitative transverse study was implemented utilizing a convenient sample of 434 nursing students from a governmental university. Data were analyzed using many descriptive and inferential statistics.

Results

Nursing students perceived AI in nursing research positively, with an overall mean score of 3.24/5 (SE = .024). Their feelings about AI were generally positive (Mean = 3.54/5; SE = .049; 95% CI = 3.45-3.64). Perceived risks of using AI in research were high (Mean = 1.59/2, SE = .016), especially concerning liability issues (Mean = 3.50/5, SE = .031), communication barriers (Mean = 3.48, SE = .035), unregulated standards (Mean = 3.37, SE = .034), privacy concerns (Mean = 3.37, SE = .034), social biases (Mean = 3.33, SE = .033), performance anxiety (Mean = 3.31, SE = .034), and mistrust in AI mechanisms (Mean = 3.28, SE = .032). The perceived benefits were also high (Mean = 3.46, SE = .030), with a strong intention to use AI-based tools (Mean = 3.52, SE = .033). Key predictors were high GPA and training in public hospitals. hospitals.

Conclusion

AI in nursing research has many benefits; however, it comes with risks that need immediate management. Nursing students' GPAs and the hospitals where they received their training were often the key factors that shaped how well they understood the use of AI in nursing research. High-achieving students who were trained in public and teaching hospitals tend to be better users of AI in nursing research.

Healthy nutrition and weight management for a positive pregnancy experience in the antenatal period: Comparison of responses from artificial intelligence models on nutrition during pregnancy

BACKGROUND

As artificial intelligence AI-supported applications become integral to web-based information-seeking, assessing their impact on healthy nutrition and weight management during the antenatal period is crucial.

OBJECTIVE

This study was conducted to evaluate both the quality and semantic similarity of responses created by AI models to the most frequently asked questions about healthy nutrition and weight management during the antenatal period, based on existing clinical knowledge.

METHODS

In this study, a cross-sectional assessment design was used to explore data from 3 AI models (GPT-4, MedicalGPT, Med-PaLM). We directed the most frequently asked questions about nutrition during pregnancy, obtained from the American College of Obstetricians and Gynecologists (ACOG) to each model in a new and single session on October 21, 2023, without any prior conversation. Immediately after, instructions were given to the AI models to generate responses to these questions. The responses created by AI models were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale. Additionally, to assess the semantic similarity between answers to 31 pregnancy nutrition-related frequently asked questions sourced from the ACOG and responses from AI models we evaluated cosine similarity using both WORD2VEC and BioLORD-2023.

RESULTS

Med-PaLM outperformed GPT-4 and MedicalGPT in response quality (mean = 3.93), demonstrating superior clinical accuracy over both GPT-4 (p = 0.016) and MedicalGPT (p = 0.001). GPT-4 had higher quality than MedicalGPT (p = 0.027). The semantic similarity between ACOG and Med-PaLM is higher with WORD2VEC (0.92) compared to BioLORD-2023 (0.81), showing a difference of +0.11. The similarity scores for ACOG-MedicalGPT and ACOG-GPT-4 are similar across both models, with minimal differences of -0.01. Overall, WORD2VEC has a slightly higher average similarity (0.82) than BioLORD-2023 (0.79), with a difference of +0.03.

CONCLUSIONS

Despite the superior performance of Med-PaLM, there is a need for further evidence-based research and improvement in the integration of AI in healthcare due to varying AI model performances.

Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events

PURPOSE

To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).

DESIGN

This was a retrospective observational study from a single academic hospital over six calendar years (2016-2021). Machine learning was used to examine patients with an NSI compared to those without.

METHODS

Inclusion criteria: all adult inpatient admissions (2016-2021). Three approaches were used to analyze the NSI group compared to the No-NSI group. In the univariate analysis, descriptive statistics, and absolute standardized differences (ASDs) were employed to compare the demographics and clinical variables of patients who experienced a NSI and those who did not experience any NSIs. For the multivariate analysis, a light grading boosting machine (LightGBM) model was utilized to comprehensively examine the relationships associated with the development of an NSI. Lastly, a simulation study was conducted to quantify the strength of associations obtained from the machine learning model.

RESULTS

From 163,507 admissions, 4643 (2.8%) were associated with at least one NSI. The mean, standard deviation (SD) age was 59.5 (18.2) years, males comprised 82,397 (50.4%). Non-Hispanic White 84,760 (51.8%), non-Hispanic Black 8703 (5.3%), non-Hispanic Asian 23,368 (14.3%), non-Hispanic Other 14,284 (8.7%), and Hispanic 30,271 (18.5%). Race and ethnicity alone were not associated with occurrence of an NSI. The NSI group had a statistically significant longer length of stay (LOS), longer intensive care unit (ICU) LOS, and was more likely to have an emergency admission compared to the group without an NSI. The simulation study results demonstrated that likelihood of NSI was higher in patients admitted under the major diagnostic categories (MDC) associated with circulatory, digestive, kidney/urinary tract, nervous, and infectious and parasitic disease diagnoses.

CONCLUSION

In this study, race/ethnicity was not associated with the risk of an NSI event. The risk of an NSI event was associated with emergency admission, longer LOS, longer ICU-LOS and certain MDCs (circulatory, digestive, kidney/urinary, nervous, infectious, and parasitic diagnoses).

CLINICAL RELEVANCE

Machine learning methodologies provide a new mechanism to investigate NSI events through the lens of health equity/disparity. Understanding which patients are at higher risk for adverse outcomes can help hospitals improve nursing care and prevent NSI injury and harm.

Assessing Visitor Expectations of AI Nursing Robots in Hospital Settings: Cross-Sectional Study Using the Kano Model

Background

Globally, the rates at which the aging population and the prevalence of chronic diseases are increasing are substantial. With declining birth rates and a growing percentage of older individuals, the demand for nursing staff is steadily rising. However, the shortage of nursing personnel has been a long-standing issue. In recent years, numerous researchers have advocated for the implementation of nursing robots as a substitute for traditional human labor.

Objective

This study analyzes hospital visitors' attitudes and priorities regarding the functional areas of artificial intelligence (AI) nursing robots based on the Kano model. Building on this analysis, recommendations are provided for the functional optimization of AI nursing robots, aiming to facilitate their adoption in the nursing field.

Methods

Using a random sampling method, 457 hospital visitors were surveyed between December 2023 and March 2024 to compare the differences in demand for AI nursing robot functionalities among the visitors.

Results

A comparative analysis of the Kano attribute quadrant diagrams showed that visitors seeking hospitalization prioritized functional aspects that enhance medical activities. In contrast, visitors attending outpatient examinations focused more on functional points that assist in medical treatment. Additionally, visitors whose purpose was companionship and care emphasized functional aspects that offer psychological and life support to patients.

Conclusions

AI nursing robots serve various functional areas and cater to diverse audience groups. In the future, it is essential to thoroughly consider users' functional needs and implement targeted functional developments to maximize the effectiveness of AI nursing robots.

Relationship Between Individual Innovativeness Levels and Attitudes Toward Artificial Intelligence Among Nursing and Midwifery Students

The aim of this study is to explore the connection between individual innovativeness levels and attitudes toward artificial intelligence among nursing and midwifery students. Data were collected from 500 nursing and midwifery students studying at a university in Türkiye. The data gathered between November and December 2023 involved a Personal Information Form, the Individual Innovation Scale, and the General Attitudes toward Artificial Intelligence Scale. Data analysis used descriptive statistics, independent-samples t test, analysis of variance, Bonferroni test, and logistic regression models. Students' average Individual Innovativeness Scale score was 59.47 ± 7.23. Consequently, it was determined that students' individual innovativeness levels were inadequate, placing them in the questioning group. Students demonstrated positive attitudes toward artificial intelligence, with General Attitudes toward Artificial Intelligence Scale-positive scores at a good level (42.67 ± 7.10) and negative attitudes at an average level (24.08 ± 5.81). A significant, positive relationship was found between Individual Innovation Scale and General Attitudes toward Artificial Intelligence Scale total scores ( P < .001). The individual innovation level of students proved to be a significant predictor of attitudes toward artificial intelligence ( P < .001). Students' individual innovativeness levels positively influence their attitudes toward artificial intelligence. However, it was identified that students' individual innovativeness levels are not sufficient and require improvement.

Impact of Artificial Intelligence-Based Technology on Nurse Management: A Systematic Review

Aim: To describe the use of artificial intelligence (AI) by nurse managers to enhance management, leadership, and healthcare outcomes. Background: AI represents a significant transformation in healthcare management by enhancing decision-making, communication, and resource optimization. However, the integration and strategic application of AI in nursing management are underexplored, particularly regarding its impact on leadership roles and healthcare delivery. Methods: Methodological guidelines described by PRISMA were followed, and quality was assessed using the Joanna Briggs Institute (JBI) methodology. The databases searched included the Web of Science, Scopus, CINAHLi, and PubMed. The review included quantitative, qualitative, and mixed-method studies published between January 2015 and April 2024. Results: Fourteen studies were selected for the review. The key findings indicate that AI technologies facilitate better resource management, risk assessment, and decision-making. AI also supports nurse managers in leading changes, enhancing communication, and optimizing administrative tasks. Conclusion: AI has been progressively integrated into nursing management, demonstrating significant benefits in operational efficiency, decision support, and leadership enhancement. However, challenges, such as resistance to technological change and ethical complexities, need to be addressed. Implications for Nursing Management: Specific training programs for nurse managers are essential to optimize the integration of AI. Such programs should focus on the management of AI applications and data analyses. In addition, creating interdisciplinary groups involving nurse managers, AI developers, and nursing staff is crucial for tailoring AI solutions to meet the unique needs of healthcare settings.

Emerging Artificial Intelligence-Based Pedagogies in Didactic Nursing Education: A Scoping Review

BACKGROUND

Artificial intelligence pedagogies are increasingly commonplace in health care education, and limited information guides their application in didactic nursing environments.

PURPOSE

To examine the current state of artificial intelligence-based pedagogies used in didactic nursing education.

DESIGN

The review was conducted using Arksey and O'Malley's scoping review framework and the Joanna Briggs Institute's System for the Unified Management, Assessment, and Review of Information platform. Literature is reported using the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews.

METHODS

The review included articles published between January 1, 2013, and July 23, 2024, in MEDLINE (via PubMed), Cumulative Index to Nursing and Allied Health Literature, Education Resources Information Center, World Science, and Google Scholar. Two reviewers independently assessed all articles.

RESULTS

Themes for the 16 included articles were generative artificial intelligence and pairing artificial intelligence with other pedagogical strategies.

CONCLUSIONS

More research is needed to examine artificial intelligence-based pedagogies in didactic nursing education.

The research hotspots and theme trends of artificial intelligence in nurse education: A bibliometric analysis from 1994 to 2023

OBJECTIVES

To explore research hotspots and theme trends in artificial intelligence in nurse education using bibliometric analysis.

DESIGN

Bibliometric analysis.

DATA SOURCES

Literature from the Web of Science Core Collection from the time of construction to October 31, 2023 was searched.

REVIEW METHODS

Analyses of countries, authors, institutions, journals, and keywords were conducted using Bibliometrix (based on R language), CiteSpace, the online analysis platform (bibliometric), Vosviewer, and Pajek.

RESULTS

A total of 135 articles with a straight upward trend over the last three years were retrieved. By fitting the curve R2 = 0.6022 (R2 > 0.4), we predicted that the number of annual articles is projected to grow in the coming years. The United States (n = 38), the National University of Singapore (n = 16), Professor Jun Ota (n = 8), and Nurse Education Today (n = 14) are the countries, institutions, authors, and journals that contributed to the most publications, respectively. Collaborative network analysis revealed that 32 institutional and 64 author collaborative teams were established. We identified ten high-frequency keywords and nine clusters. We categorized the research hotspots of artificial intelligence in nurse education into three areas: (1) Artificial intelligence-enhanced simulation robots, (2) machine learning and data mining, and (3) large language models based on natural language processing and deep learning. By analyzing the temporal and spatial evolution of keywords and burst detection, we found that future research trends may include (1) expanding and deepening the application of AI technology, (2) assessment of behavioral intent and educational outcomes, and (3) moral and ethical considerations.

CONCLUSIONS

Future research should be conducted on technology applications, behavioral intent, ethical policy, international cooperation, interdisciplinary cooperation, and sustainability to promote the continued development and innovation of AI in nurse education.

The effect of artificial intelligence supported case analysis on nursing students' case management performance and satisfaction: A randomized controlled trial

BACKGROUND

Rapid developments in artificial intelligence have begun to necessitate changes and transformations in nursing education.

OBJECTIVE

This study aimed to evaluate the impact of an artificial intelligence-supported case created in the in-class case analysis lecture for nursing students on students' case management performance and satisfaction.

DESIGN

This study was a randomized controlled trial.

METHOD

The study involved 188 third-year nursing students randomly assigned to the AI group (n=94) or the control group (n=94). An information form, case evaluation form, knowledge test and Mentimeter application were used to assess the students' case management performance and nursing diagnoses. The level of satisfaction with the case analysis lecture was evaluated using the VAS scale.

RESULTS

The case management performance scores of the students in the artificial intelligence group were significantly higher than those of the control group (p<0.05). There was no statistically significant difference in satisfaction levels between the artificial intelligence (AI) group and the control group (p>0.05).

CONCLUSIONS

The study's results indicated that AI-supported cases improved students' case management performance and were as effective as instructor-led cases regarding satisfaction with the case analysis lecture, focus and interest in the case. The integration of artificial intelligence into traditional nursing education curricula is recommended.

CLINICAL TRIALS REGISTRATION NUMBER

https://register.

CLINICALTRIALS

gov; (NCT06443983).

Systematic Review of the Application of Artificial Intelligence in Healthcare and Nursing Care

This systematic review explores the complex relationship between artificial intelligence (AI) and healthcare, with an explicit focus on nursing care. Examining a range of studies from 2020, the research investigates the impact of AI on clinical decision-making, patient care and healthcare administration. Through a comprehensive literature review, the study highlights the potential benefits of AI integration in improving the efficiency and efficacy of healthcare. AI technologies offer opportunities for personalised patient care, predictive analytics and enhanced clinical processes, with the ultimate aim of transforming the healthcare system. However, ethical considerations and regulatory frameworks are crucial, emphasising patient privacy, autonomy and data security. The findings underscore the need for transparency, accountability and fairness in the application of AI in healthcare. While AI promises to improve patient outcomes and streamline healthcare delivery, careful consideration of ethical implications and regulatory compliance are essential for responsible implementation.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

Background

With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption.

Objectives

This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings.

Methods

We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail.

Results

Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare.

Conclusions

The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Possibilities and ethical issues of entrusting nursing tasks to robots and artificial intelligence

In recent years, research in robotics and artificial intelligence (AI) has made rapid progress. It is expected that robots and AI will play a part in the field of nursing and their role might broaden in the future. However, there are areas of nursing practice that cannot or should not be entrusted to robots and AI, because nursing is a highly humane practice, and therefore, there would, perhaps, be some practices that should not be replicated by robots or AI. Therefore, this paper focuses on several ethical concepts (advocacy, accountability, cooperation, and caring) that are considered important in nursing practice, and examines whether it is possible to implement these ethical concepts in robots and AI by analyzing the concepts and the current state of robotics and AI technology. Advocacy: Among the components of advocacy, safeguarding and apprising can be more easily implemented, while elements that require emotional communication with patients, such as valuing and mediating, are difficult to implement. Accountability: Robotic nurses with explainable AI have a certain level of accountability. However, the concept of explanation has problems of infinite regression and attribution of responsibility. Cooperation: If robot nurses are recognized as members of a community, they require the same cooperation as human nurses. Caring: More difficulties are expected in care-receiving than in caregiving. However, the concept of caring itself is ambiguous and should be explored further. Accordingly, our analysis suggests that, although some difficulties can be expected in each of these concepts, it cannot be said that it is impossible to implement them in robots and AI. However, even if it were possible to implement these functions in the future, further study is needed to determine whether such robots or AI should be used for nursing care. In such discussions, it will be necessary to involve not only ethicists and nurses but also an array of society members.

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

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.

The impact of mandated use early warning system tools on the development of nurses' higher-order thinking: A systematic review

AIM

Ascertain the impact of mandated use of early warning systems (EWSs) on the development of registered nurses' higher-order thinking.

DESIGN

A systematic literature review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklist (Page et al., 2021).

DATA SOURCES

CINAHL, Medline, Embase, PyscInfo.

REVIEW METHODS

Eligible articles were quality appraised using the MMAT tool. Data extraction was conducted independently by four reviewers. Three investigators thematically analysed the data.

RESULTS

Our review found that EWSs can support or suppress the development of nurses' higher-order thinking. EWS supports the development of higher-order thinking in two ways; by confirming nurses' subjective clinical assessment of patients and/or by providing a rationale for the escalation of care. Of note, more experienced nurses expressed their view that junior nurses are inhibited from developing effective higher-order thinking due to reliance on the tool.

CONCLUSION

EWSs facilitate early identification of clinical deterioration in hospitalised patients. The impact of EWSs on the development of nurses' higher-order thinking is under-explored. We found that EWSs can support and suppress nurses' higher-order thinking. EWS as a supportive factor reinforces the development of nurses' heuristics, the mental shortcuts experienced clinicians call on when interpreting their subjective clinical assessment of patients. Conversely, EWS as a suppressive factor inhibits the development of nurses' higher-order thinking and heuristics, restricting the development of muscle memory regarding similar presentations they may encounter in the future. Clinicians' ability to refine and expand on their catalogue of heuristics is important as it endorses the future provision of safe and effective care for patients who present with similar physiological signs and symptoms.

IMPACT

This research impacts health services and education providers as EWS and nurses' development of higher-order thinking skills are essential aspects of delivering safe, quality care.

NO PATIENT OR PUBLIC CONTRIBUTION

This is a systematic review, and therefore, comprises no contribution from patients or the public.

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

BACKGROUND

Although the use of artificial intelligence (AI)-based technologies, such as AI-based decision support systems (AI-DSSs), can help sustain and improve the quality and efficiency of care, their deployment creates ethical and social challenges. In recent years, a growing prevalence of high-level guidelines and frameworks for responsible AI innovation has been observed. However, few studies have specified the responsible embedding of AI-based technologies, such as AI-DSSs, in specific contexts, such as the nursing process in long-term care (LTC) for older adults.

OBJECTIVE

Prerequisites for responsible AI-assisted decision-making in nursing practice were explored from the perspectives of nurses and other professional stakeholders in LTC.

METHODS

Semistructured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists, and care centralists. A total of 2 imaginary scenarios about AI-DSSs were developed beforehand and used to enable participants articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. In addition, 6 high-level principles for responsible AI were used as probing themes to evoke further consideration of the risks associated with using AI-DSSs in LTC. Furthermore, the participants were asked to brainstorm possible strategies and actions in the design, implementation, and use of AI-DSSs to address or mitigate these risks. A thematic analysis was performed to identify the opportunities and risks of AI-assisted decision-making in nursing practice and the associated prerequisites for responsible innovation in this area.

RESULTS

The stance of care professionals on the use of AI-DSSs is not a matter of purely positive or negative expectations but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of the prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to the early identification of care needs, guidance in devising care strategies, shared decision-making, and the workload of and work experience of caregivers. To optimally balance the opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in nursing practice were identified: (1) regular deliberation on data collection; (2) a balanced proactive nature of AI-DSSs; (3) incremental advancements aligned with trust and experience; (4) customization for all user groups, including clients and caregivers; (5) measures to counteract bias and narrow perspectives; (6) human-centric learning loops; and (7) the routinization of using AI-DSSs.

CONCLUSIONS

The opportunities of AI-assisted decision-making in nursing practice could turn into drawbacks depending on the specific shaping of the design and deployment of AI-DSSs. Therefore, we recommend considering the responsible use of AI-DSSs as a balancing act. Moreover, considering the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address the different factors important to the responsible embedding of AI-DSSs in practice.

Nursing workload: use of artificial intelligence to develop a classifier model

OBJECTIVE

to describe the development of a predictive nursing workload classifier model, using artificial intelligence.

METHOD

retrospective observational study, using secondary sources of electronic patient records, using machine learning. The convenience sample consisted of 43,871 assessments carried out by clinical nurses using the Perroca Patient Classification System, which served as the gold standard, and clinical data from the electronic medical records of 11,774 patients, which constituted the variables. In order to organize the data and carry out the analysis, the Dataiku® data science platform was used. Data analysis occurred in an exploratory, descriptive and predictive manner. The study was approved by the Ethics and Research Committee of the institution where the study was carried out.

RESULTS

the use of artificial intelligence enabled the development of the nursing workload assessment classifier model, identifying the variables that most contributed to its prediction. The algorithm correctly classified 72% of the variables and the area under the Receiver Operating Characteristic curve was 82%.

CONCLUSION

a predictive model was developed, demonstrating that it is possible to train algorithms with data from the patient's electronic medical record to predict the nursing workload and that artificial intelligence tools can be effective in automating this activity.

Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models

BACKGROUND

With the rapidly advancing technological landscape of health care, evaluating the potential use of artificial intelligence (AI) models to prepare nursing care plans is of great importance.

PURPOSE

The purpose of this study was to evaluate the quality of nursing care plans created by AI for the management of postpartum hemorrhage (PPH).

METHODS

This cross-sectional exploratory study involved creating a scenario for an imaginary patient with PPH. Information was put into 3 AI platforms (GPT-4, LaMDA, Med-PaLM) on consecutive days without prior conversation. Care plans were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale.

RESULTS

Med-PaLM exhibited superior quality in developing the care plan compared with LaMDA ( Z = 4.354; P = .000) and GPT-4 ( Z = 3.126; P = .029).

CONCLUSIONS

Our findings suggest that despite the strong performance of Med-PaLM, AI, in its current state, is unsuitable for use with real patients.

Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study

Artificial intelligence (AI) decision support systems in pediatric healthcare have a complex application background. As an AI decision support system (AI-DSS) can be costly, once applied, it is crucial to focus on its performance, interpret its success, and then monitor and update it to ensure ongoing success consistently. Therefore, a set of evaluation indicators was explicitly developed for AI-DSS in pediatric healthcare, enabling continuous and systematic performance monitoring. The study unfolded in two stages. The first stage encompassed establishing the evaluation indicator set through a literature review, a focus group interview, and expert consultation using the Delphi method. In the second stage, weight analysis was conducted. Subjective weights were calculated based on expert opinions through analytic hierarchy process, while objective weights were determined using the entropy weight method. Subsequently, subject and object weights were synthesized to form the combined weight. In the two rounds of expert consultation, the authority coefficients were 0.834 and 0.846, Kendall's coordination coefficient was 0.135 in Round 1 and 0.312 in Round 2. The final evaluation indicator set has three first-class indicators, fifteen second-class indicators, and forty-seven third-class indicators. Indicator I-1(Organizational performance) carries the highest weight, followed by Indicator I-2(Societal performance) and Indicator I-3(User experience performance) in the objective and combined weights. Conversely, 'Societal performance' holds the most weight among the subjective weights, followed by 'Organizational performance' and 'User experience performance'. In this study, a comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented. Continuous evaluation still requires long-term data collection to optimize the weight proportions of the established indicators.

AI Quality Standards in Health Care: Rapid Umbrella Review

BACKGROUND

In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies.

OBJECTIVE

This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period.

METHODS

We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies.

RESULTS

We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications.

CONCLUSIONS

Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.

Smart diabetic foot ulcer scoring system

Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing a significant risk to diabetes patients, including the potential for amputations, highlighting the urgent need for improved diagnostic tools and care standards in the field. To address this issue, the objective of this study was to develop and evaluate the Smart Diabetic Foot Ulcer Scoring System, ScoreDFUNet, which incorporates artificial intelligence (AI) and image analysis techniques, aiming to enhance the precision and consistency of diabetic foot ulcer assessment. ScoreDFUNet demonstrates precise categorization of DFU images into "ulcer," "infection," "normal," and "gangrene" areas, achieving a noteworthy accuracy rate of 95.34% on the test set, with elevated levels of precision, recall, and F1 scores. Comparative evaluations with dermatologists affirm that our algorithm consistently surpasses the performance of junior and mid-level dermatologists, closely matching the assessments of senior dermatologists, and rigorous analyses including Bland-Altman plots and significance testing validate the robustness and reliability of our algorithm. This innovative AI system presents a valuable tool for healthcare professionals and can significantly improve the care standards in the field of diabetic foot ulcer assessment.

The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine

In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?

Background

Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care.

Objective

This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care.

Methods

Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation.

Results

A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan.

Conclusion

This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Backgrounds

With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers.

Aim

To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward.

Methods

Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords.

Results

According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement.

Conclusion

Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.

Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review

OBJECTIVE

The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.

[Digital technologies in nursing-what can they achieve?]

Digital care technologies are becoming increasingly important in long-term care. They encompass all technologies that change processes and products by means of networking and/or sensor technology and include artificial intelligence, that is, processes, methods, and algorithms for learning by means of data and enabling meaningful decisions based on this. Their application ranges from the promotion of professional collaboration, control and management, knowledge acquisition and transfer, interaction and relationships to physical caregiving.Digital care technologies have the potential to simultaneously increase the quality of care and improve working conditions in care. However, there are obstacles to this at various levels: The development of these technologies is often driven by technical possibilities, resulting in products that do not provide any concrete benefits in routine nursing care. During implementation, only the operator is trained; however, there is no organizational development for the systematic integration of these technologies into routine work. In addition, there is a lack of high-quality evaluations showing evidence of the actual benefits to routine work in order to attract potential users to these technologies. Finally, there is no sustainable financing, especially for the maintenance of these technologies.Successful digitization in long-term care therefore requires that technology developers and users, as well as policymakers and scientists, jointly overcome these obstacles. This implies that caregivers are involved in the development process from the outset (co-creation) but also that spaces are created where the effect of digital care technologies can be evaluated in routine caregiving.

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review

Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.

The ChatGPT effect and transforming nursing education with generative AI: Discussion paper

AIM

The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process.

BACKGROUND

The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation.

DESIGN

Discussion paper.

METHODS

We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results.

RESULTS

In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association - International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association - International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases.

CONCLUSIONS

Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking.

The application and use of artificial intelligence in cancer nursing: A systematic review

PURPOSE

Artificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational techniques are employed in cancer nursing to inform clinical practice. This review aimed to identify and synthesise evidence on artificial intelligence in cancer nursing.

METHODS

CINAHL, MEDLINE, PsycINFO, and PubMed were searched using key terms between January 2010 and December 2022. Titles, abstracts, and then full texts were screened against eligibility criteria, resulting in twenty studies being included. Critical appraisal was undertaken, and relevant data extracted and analysed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed.

RESULTS

Artificial intelligence was used in numerous areas including breast, colorectal, liver, and ovarian cancer care among others. Algorithms were trained and tested on primary and secondary datasets to build predictive models of health problems related to cancer. Studies reported this led to improvements in the accuracy of predicting health outcomes or identifying variables that improved outcome prediction. While nurses led most studies, few deployed an artificial intelligence based digital tool with cancer nurses in a real-world setting as studies largely focused on developing and validating predictive models.

CONCLUSION

Electronic cancer nursing datasets should be established to enable artificial intelligence techniques to be tested and if effective implemented in digital prediction and other AI-based tools. Cancer nurses need more education on machine learning and natural language processing, so they can lead and contribute to artificial intelligence developments in oncology.