Exploring the Ethical Landscape of Artificial Intelligence in Nursing Practice: A Bibliometric Analysis

Exploring the Ethical Landscape of Artificial Intelligence in Nursing Practice: A Bibliometric Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Exploring the Ethical Landscape of Artificial Intelligence in Nursing Practice: A Bibliometric Analysis Fandro Armando Tasijawa, Widya Addiarto, Dodik Hartono, Zainal Munir, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8033889/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The integration of Artificial Intelligence (AI) into nursing practice is rapidly advancing, offering potential improvements in clinical accuracy, decision-making, and service efficiency. However, this acceleration also introduces ethical challenges related to data privacy, algorithmic transparency, accountability, and fairness in healthcare delivery. Objective: This study aims to map the scientific landscape of ethical issues surrounding the use of AI in nursing practice through a bibliometric approach. Methods: Data were retrieved from the Web of Science Core Collection on October 30, 2025, using keywords related to AI, ethics, and nursing. The inclusion criteria comprised English-language research articles and reviews published between 2019 and 2025. A total of 68 articles met the eligibility criteria and were analyzed using the Web of Science Analysis Tools, VOSviewer (v.1.6.20), and Microsoft Excel to map publication trends, author and institutional collaborations, and thematic structures based on co-authorship, bibliographic coupling, and keyword co-occurrence. Results: Publications increased sharply from one article in 2019 to 35 in 2025, indicating growing global attention to the ethical dimensions of AI in nursing. The United States, China, and the United Kingdom emerged as the leading contributors, with international researcher collaborations forming three distinct clusters. BMJ Open , Nursing Ethics , and BMC Nursing were identified as the most influential journals. The dominant keywords included artificial intelligence , nursing ethics , machine learning , decision-making , and AI literacy , forming four thematic clusters: ethics and data, generative AI and decision-making, robotics in care, and digital literacy. The most cited articles highlighted the opportunities and challenges of generative AI in clinical practice and education. Conclusion: The application of AI in nursing is rapidly expanding but is accompanied by significant ethical dilemmas. The bibliometric analysis revealed a global focus on privacy, accountability, algorithmic fairness, and nursing preparedness. Strengthening AI literacy, establishing ethical governance frameworks, and developing adaptive policies are essential to ensure the responsible and patient-centered implementation of AI technologies. Nursing artificial intelligence nursing ethics decision-making bibliometric analysis AI literacy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Artificial Intelligence (AI) has emerged as a transformative force in global healthcare, offering immense potential to enhance diagnostic accuracy, accelerate clinical decision-making, and optimize the efficiency of nursing service systems (Badawy et al. 2025b ; Hu et al. 2025 ). In nursing practice, AI functions not only as a clinical support tool through predictive analytics and decision support systems but also as an innovative mechanism in nursing education, hospital management, and data-driven research (Badawy et al. 2025b ). However, alongside these opportunities lie a range of complex ethical challenges related to data privacy, algorithmic fairness, transparency, patient autonomy, and the professional accountability of nurses (Gallagher 2024 ). Over the past decade, the adoption of AI in nursing has increased exponentially. Bibliometric mapping has revealed a significant surge in global publications on AI and nursing between 2014 and 2024, with the United States, China, and Canada emerging as the main contributors (Badawy et al. 2025a ). This growth reflects a paradigm shift from a focus on nursing informatics toward broader concerns, including ethics, digital literacy, and the governance of technology in nursing practice and education. Furthermore, global bibliometric analyses of “AI in nursing decision-making” show an annual increase of 7.6%, underscoring the growing attention toward AI integration in clinical practice and patient safety (Hu et al. 2025 ). Nevertheless, the implementation of AI in nursing is not without ethical risks. First, issues of data privacy and governance have come to the forefront, as AI systems often rely on large volumes of patient data without adequate consent mechanisms (Mohammad Amini et al. 2023 ). Second, accountability and transparency remain ambiguous—particularly regarding who is responsible for clinical decisions generated by AI-based systems (Wynn 2025 ). Third, algorithmic bias and fairness raise concerns that AI may reinforce existing healthcare disparities, particularly among vulnerable populations (Hassanein et al. 2025 ). Equally important, AI has the potential to erode core nursing values such as empathy, compassion, and human connection—the essence of nursing care (Arcadi 2025 ; Gallagher 2024 ). In nursing education, there is an urgent need to equip nursing students and practitioners with sufficient ethical digital literacy to critically assess, utilize, and monitor AI systems responsibly (De Gagne et al. 2024 ; Sengul et al. 2025 ). Correspondingly, nursing leadership is urged to develop ethical governance frameworks that ensure AI implementation remains aligned with humanistic values and social justice (Dornan 2025 ). Given these complexities, a comprehensive approach is required to map the global ethical landscape of AI in nursing. The bibliometric method provides a suitable means of conducting this analysis, as it allows for quantitative examination of publication patterns, scientific collaborations, and the evolution of research themes (Badawy et al. 2025b ; Hu et al. 2025 ). Therefore, this study aims to explore the ethical landscape of AI implementation in nursing practice by analyzing publication trends, major research domains, and the scholarly networks shaping this field. The findings are expected to provide a conceptual foundation for the development of ethical policies, education, and nursing practices in the digital era. Method This study aimed to comprehensively map the development, scientific collaboration, and thematic focus of research related to Artificial Intelligence (AI) in nursing practice, particularly from the perspective of nursing ethics. A bibliometric approach was employed, allowing for quantitative analysis of scientific publications to identify trends, collaboration patterns, and the emerging knowledge networks within this field. The bibliographic data for this study were obtained from the Web of Science Core Collection (WoSCC), which was selected as the most suitable database for bibliometric research, particularly for conducting co-citation, co-authorship, and keyword co-occurrence analyses. Data retrieval was performed on October 30, 2025, using the advanced search function in WoS with the following search query: TS = (Artificial Intelligence) AND TS = (Ethics) AND TS = (Nursing). The initial search yielded 236 articles (Fig. 1 ). To ensure data accuracy and reliability, the search process was conducted independently by two researchers, verified by two additional researchers, and finally validated by an independent reviewer. All researchers involved in data searching, screening, reviewing, and extraction worked independently to minimize subjective bias. The inclusion criteria consisted of research articles and review papers published between 2019 and 2025, written in English, and indexed in the Science Citation Index (SCI), Social Sciences Citation Index (SSCI), or Social Sciences Citation Index-Expanded (SSCI-E). Publications such as conference proceedings, editorials, book chapters, and early access articles were excluded from the analysis. Each retrieved article was manually screened to confirm its relevance to the topic of ethics and artificial intelligence in nursing, while irrelevant papers were excluded. After the multi-step screening process, 68 articles met the inclusion criteria and were retained for final analysis. All bibliographic data were exported in the “full records with cited references” format for subsequent analysis using VOSviewer. Bibliometric analysis and data visualization were conducted using three main tools: the Web of Science Analysis Tool, VOSviewer (version 1.6.20), and Microsoft Excel 2021. These tools were applied in an integrated manner to provide a comprehensive overview of publication trends, collaborative relationships among authors and institutions, and the conceptual structure of research on AI ethics in nursing practice. First, the Web of Science Analysis Tool was used to perform descriptive analyses of the data, including the identification of the most productive authors, the countries with the largest contributions, leading institutions, key journals, and the annual growth of publications. The results were visualized through graphs and diagrams to provide a quantitative understanding of research dynamics and growth patterns. Second, VOSviewer was employed to construct network-based bibliometric maps (network visualizations). This tool was used to map international collaboration networks, co-authorship relationships among researchers and institutions, and keyword co-occurrence patterns that illustrate the interconnections between ethics, nursing, and artificial intelligence. In VOSviewer visualizations, colors represent thematic clusters, connecting lines indicate the strength of relationships (link strength) among elements, and circle sizes depict the frequency or contribution level of each element within the research network. Third, Microsoft Excel 2021 was utilized for supplementary descriptive analyses and the creation of supporting visualizations such as frequency tables, bar charts, and publication trend graphs. Data exported from WoS were processed in Excel to illustrate publication dynamics, collaboration patterns, and keyword evolution over time. The visual outputs from Excel also served as the foundation for further analysis in VOSviewer, resulting in a more structured and in-depth knowledge map. Overall, the combination of these three analytical tools produced a comprehensive and integrated methodological framework for depicting the research landscape of AI ethics in nursing practice. This approach enabled the in-depth identification of scientific collaboration patterns, key thematic trends, and the developmental trajectory of this rapidly expanding field. Results Annual Distribution of Publications Table 1 presents the annual distribution of publications and citation counts from 2019 to 2025. The data reveal a significant upward trajectory in publication numbers over time, reflecting the rapidly growing research interest in this topic. In 2019, there was only one article (1.47%) with 29 citations, marking the initial phase of exploration in this field. In 2020, the number of publications increased to two articles (2.94%) with 118 citations, followed by another two articles (2.94%) in 2021, which received 182 citations—indicating growing scholarly recognition and engagement. The expansion became more pronounced in 2022, with five articles (7.35%) and 210 citations, representing a phase of steady growth. A substantial surge occurred in 2023, with seven articles (10.29%) and a total of 1,897 citations, demonstrating the heightened academic attention toward this research area. The peak of productivity was observed in 2024, with 16 articles (23.52%) receiving 219 citations. Meanwhile, 2025 recorded the sharpest rise in publication numbers, totaling 35 articles (51.47%), although the citation count (26) remained relatively low—likely because most publications from that year were still recent and had not yet accumulated extensive citations. Overall, the findings indicate an exponential growth trend in publications over the past seven years, underscoring the increasing global relevance and scholarly interest in the ethical dimensions of Artificial Intelligence in nursing practice. Table 1 Annual trends in publication output and citation frequency between 2019 and 2025 Year Total Articles Total number of citations % of 2019 1 29 1.47 2020 2 118 2.94 2021 2 182 2.94 2022 5 210 7.35 2023 7 1897 10.29 2024 16 219 23.52 2025 35 26 51.47 Citation Trends The top ten articles in this analysis reveal a strong and consistent research trend concerning the application and ethical implications of Artificial Intelligence (AI) in nursing. The most influential publication, titled “So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy” by Dwivedi et al. ( 2023 ) published in the International Journal of Information Management , received a total of 1,746 citations, averaging 582 citations per year. This underscores the considerable scholarly attention devoted to generative AI and its far-reaching implications for research and policy development. The second most cited study, conducted by Ronquillo et al. ( 2021 ), focuses on identifying priorities and opportunities for AI integration within nursing, followed by the work of Kwak et al. ( 2022 ), which investigates the effects of ethical awareness, attitudes, and self-efficacy on nursing students’ behavioral intentions. Other notable contributions, such as those by Stokes and Palmer ( 2020 ), examine the ethical dimensions of task-sharing between humans and AI, while Zhu et al. ( 2022 ) address ethical concerns in elderly care supported by smart home technologies. Publications appearing in high-impact journals such as the Journal of Advanced Nursing , BMC Nursing , Nursing Philosophy , and Nursing Ethics demonstrate that AI in nursing has emerged as a multidisciplinary domain encompassing ethical, educational, and clinical perspectives. Overall, the high citation frequency of recent publications (2023–2024) indicates that this topic is evolving rapidly, with research trends increasingly emphasizing the integration of technology, human values, and professional accountability in navigating the digital transformation of the nursing profession. Table 2 Top 10 cited studies Rank Title Authors and Year Source Average citation per year Total Citation 1 So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy (Dwivedi et al. 2023 ) International Journal of Information Management 582 1,746 2 Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative (Ronquillo et al. 2021 ) Journal of Advanced Nursing 36.5 182 3 Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students' behavioral intentions (Kwak et al. 2022 ) BMC Nursing 27.5 110 4 Artificial Intelligence and Robotics in Nursing: Ethics of Caring as a Guide to Dividing Tasks Between AI and Humans (Stokes and Palmer 2020 ) Nursing Philosophy 15.5 93 5 Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare (Yelne et al. 2023 ) Cureus 26 78 6 Ethical issues of smart home-based elderly care: A scoping review (Zhu et al. 2022 ) Journal of Nursing Management 8.6 43 7 Robots and Robotics in Nursing (Soriano et al. 2022 ) Healthcare 8.75 35 8 Medical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis (Amiri et al. 2024 ) BMC Medical Education 17 34 9 Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate (Mohammad Amini et al. 2023 ) Machine Learning and Knowledge Extraction 11.33 34 10 Cyberethics in nursing education: Ethical implications of artificial intelligence (De Gagne et al. 2024 ) Nursing Ethics 9.67 29 Research Area This research area analysis reveals that contributions to this theme are predominantly concentrated in the field of Nursing, which accounts for the largest share—50% of total publications. This finding underscores that studies on the ethics of Artificial Intelligence (AI) are primarily grounded in the context of nursing practice. The scope of research also overlaps with General Internal Medicine (16.7%), indicating that the application of AI in general healthcare settings likewise raises relevant ethical concerns. In addition, publications in Education Educational Research, Health Care Sciences Services, and Social Sciences Other Topics each account for 8.8%, suggesting that ethical studies on AI extend beyond clinical aspects to include dimensions of education, service governance, and social perspectives. Further contributions are also evident in Computer Science and Medical Informatics (each 5.8%), reflecting the involvement of technological disciplines in deepening the understanding of AI’s ethical dynamics within the nursing sector, including challenges related to adoption and system integration. Meanwhile, research within Public Environmental Occupational Health (4.4%), Business Economics (2.9%), and Area Studies (1.4%) demonstrates broader interdisciplinary exploration, highlighting that the discourse on AI ethics in nursing has evolved into a multidisciplinary field encompassing wider systemic implications. Table 3 Top 10 research areas in AI ethics in nursing Research area Number of publications % Nursing 34 50.0 General Internal Medicine 11 16.7 Education Educational Research 6 8.8 Health Care Sciences Services 6 8.8 Social Science Other Topics 6 8.8 Computer Science 4 5.8 Medical Informatics 4 5.8 Public Environmental Occupational Health 3 4.4 Business Economics 2 2.9 Area Studies 1 1.4 Most Prolific Journals The bibliographic analysis highlights the journals most frequently cited in studies related to AI and nursing. The visualization shows that BMJ Open , Nursing Ethics , and BMC Nursing have the largest nodes, indicating their roles as the most influential and frequently referenced journals in the literature. Larger node sizes represent higher citation frequencies, while the thickness of the connecting lines between nodes reflects strong referential relationships among these journals. Cluster analysis reveals several significant groupings. The green cluster emphasizes journals such as BMJ Open , Journal of Nursing Management , and Journal of Advanced Nursing , which generally discuss ethics, management, and clinical practice within the context of AI implementation. This cluster represents a research direction that underscores the importance of ethical governance and technological integration within nursing care systems. The red cluster includes journals such as BMC Nursing , Sage Open Nursing , Creative Nursing , and Advances in Skin & Wound Care . Its primary focus lies on education, innovation, and the development of AI-based learning models in nursing. The yellow cluster comprises journals such as BMC Medical Education and Frontiers in Education , which highlight the role of AI in health education and nursing training. Meanwhile, the blue and purple clusters—featuring Frontiers in Digital Health and ACM Transactions on Computer-Human Interaction —illustrate the intersection between nursing and computer science, revealing a growing trend toward interdisciplinary research that bridges digital technology, human–computer interaction, and healthcare. Overall, these citation patterns demonstrate that AI research in nursing is not solely driven by technological innovation but is also deeply grounded in ethical discourse and professional education literature. Consequently, research developments in this field increasingly emphasize the responsible and human-centered integration of technology into nursing practice and healthcare education. Most Prolific Authors Table 4 presents a list of the most productive authors in the analyzed research field, including their affiliated countries, home institutions, total number of published articles, and their respective percentages of total publications. Based on the data, it is evident that Michalowski M from the University of Minnesota, United States, ranks first with a total of three articles (4.41%), indicating the most substantial contribution among all identified authors. Several other authors show comparable productivity levels, each having published two articles (2.94%). They represent diverse countries and reputable institutions, reflecting the global distribution of research in this area. For example, Alhur AA from the University of Hail (Saudi Arabia), Chu CH from the University of Toronto (Canada), Dillard-Wright J from the University of Massachusetts Amherst (USA), and El Arab RA from Almoosa College of Health Sciences (Saudi Arabia) are among them. Notably, contributions from Europe and Scandinavia are also significant, represented by Peltonen LM from the University of Eastern Finland (Finland) and Sagbakken M from Oslo Metropolitan University (Norway). From other regions, Shaban M from the University of Leicester (United Kingdom) and Topaz M from Columbia University (USA) demonstrate valuable contributions to the advancement of this research theme. Overall, the findings indicate that research in this field is multinational and collaborative, with the United States emerging as the most dominant country in terms of both author representation and institutional affiliation. Although the number of publications per author remains relatively modest (two to three articles), this pattern suggests that the research area is still developing and is characterized by cross-institutional and international collaboration. Table 4 Leading 10 authors Name of the author Country of affiation Affiliated institution Total number of published articles % Michalowski M USA University of Minnesota 3 4.41 Alhur AA Saudi Arabia University of Hail 2 2.94 Chu CH Canada University of Toronto 2 2.94 Dillard-Wright J USA University of Massachusetts Amherst 2 2.94 El Arab RA Saudi Arabia Almoosa College of Health Sciences 2 2.94 Peltonen LM Finland University of Eastern Finland 2 2.94 Pruinelli L USA University of Florida 2 2.94 Sagbakken M Norway Oslo Metropolitan University 2 2.94 Shaban M United Kingdom University of Leicester 2 2.94 Topaz M United States Columbia University 2 2.94 Collaborations Among Authors The author collaboration network map, involving 401 researchers, reveals three main clusters that interact based on the strength of their collaborative relationships (Fig. 3 ). The first cluster (blue) is led by Michalowski, Martin, with key collaborators such as Topaz, Maxim, Pruinelli, Lisiane, and Peltonen, Laura-Maria, focusing on the application of information technology and data analytics in nursing practice. The second cluster (red) is centered around Sanna Salantera and Ana Beduschi, who frequently collaborate with researchers including Nancy Walton, Suzanne Bakken, and Nicholas Hardiker, emphasizing ethical issues, innovations in information systems, and the implementation of digital health in nursing. Meanwhile, the third cluster (green) is led by Chu, Charlene H., who acts as a key link between European and Asian research networks through collaborations with Zeng, Yingchun, Yang, Chengyue, Liu, Tao, and Niu, Yanping. The dominant research theme within this cluster focuses on the adoption of artificial intelligence and digital technologies in nursing care. The central position of Chu, Charlene H., connecting multiple clusters, underscores the strength of international collaboration networks and highlights the crucial role of cross-continental researchers in facilitating knowledge exchange and advancing innovation in the field of Artificial Intelligence in Nursing. Most Prolific Countries Figure 3 illustrates the collaborative relationships among countries. The first cluster (red) is led by the United States (USA), identified as the most productive and influential country, showing strong collaborations with Saudi Arabia, Canada, Turkey, and Japan. This cluster reflects research dominance focused on technological innovation, the implementation of intelligent systems, and AI-based nursing education. Meanwhile, the second cluster (green) comprises European and Asia-Pacific countries, with China (People’s Republic of China), the United Kingdom, Australia, and Germany serving as major research hubs. This cluster demonstrates extensive collaboration with Singapore, India, the Netherlands, and Italy, indicating strong integration of AI research across these regions. The interconnections between the two clusters reveal an extensive global research network, where the United States and China function as key nodes facilitating cross-continental collaborations. Overall, the map underscores that AI research in nursing is international and multidisciplinary in scope, with major scientific power centers distributed across North America, Europe, and Asia. Keywords The visualization analysis of keywords using VOSviewer reveals a complex yet interconnected thematic structure, illustrating the direction and focus of research related to artificial intelligence (AI) in the field of nursing. The largest node on the map indicates that the term “artificial intelligence” serves as the central hub within the network, signifying that this topic is the dominant theme and focal point in the analyzed literature. Based on the clustering results, four major groups were identified, each reflecting distinct research trends. The first cluster, marked in yellow, represents the connection between artificial intelligence, data technology, and professional ethics. Keywords such as machine learning , big data , nursing ethics , and health care suggest that research in this group focuses on the application of advanced analytical technologies and moral issues within healthcare contexts. This cluster reflects the nursing profession’s transition toward data-driven practice and ethical accountability in the use of AI. The second cluster, shown in red, centers on ethics , technology , and decision-making , encompassing keywords such as ChatGPT , generative AI , and anxiety . This theme highlights emerging discourses on the ethics of generative technologies—particularly ChatGPT—in clinical decision-making and nursing education. The association with anxiety indicates growing concern about the psychological impacts and apprehension among healthcare workers regarding automation and artificial intelligence. The green cluster represents more practical themes related to nursing , robots , care , and students . This group focuses on the implementation of robotics and AI-based technologies to enhance the quality of nursing services and improve learning within health education institutions. Meanwhile, the blue cluster reflects a research orientation emphasizing AI literacy , nursing students , and clinical decision support , underscoring the importance of digital literacy and the readiness of nursing professionals to engage with AI integration in practice. Overall, this visualization demonstrates that research trends in AI and nursing revolve around three major axes: the integration of AI technology into education and clinical practice, the exploration of its ethical and emotional dimensions, and the development of AI literacy among nursing students and professionals. Thus, artificial intelligence emerges not merely as a technological subject but also as a social and ethical phenomenon that profoundly influences the transformation of the nursing discipline. Discussion The integration of artificial intelligence (AI) into nursing practice presents a paradox between its immense potential benefits and its complex ethical challenges. Bibliometric evidence indicates an exponential growth in research interest on this topic. Publications have increased significantly from just one article in 2019 to 35 articles in 2025, reflecting a sharp rise in academic attention toward the ethical dimensions of AI use in nursing. The largest citation spike occurred in 2023, with 1,897 citations, underscoring the high relevance of this issue within scientific and interdisciplinary communities. The literature highlights that AI can enhance patient monitoring, support clinical reasoning, and reduce nurses’ administrative burdens (Nashwan et al. 2024 ; Watson 2024 ). The bibliometric findings reinforce this perspective—the most influential article by Dwivedi et al. ( 2023 ) underscores the vast opportunities of generative AI for practice, research, and policy, receiving 1,746 citations, the highest within the analyzed dataset. Several other studies similarly identify ethical concerns, user readiness, and the role of AI in clinical decision-making as central themes in the global discourse(Kwak et al. 2022 ; Ronquillo et al. 2021 ; Stokes and Palmer 2020 ). However, these developments raise fundamental questions about how professional ethics can be upheld in an increasingly sophisticated technological ecosystem. The ethical principles of autonomy, beneficence, non-maleficence, and justice remain the core normative framework guiding AI implementation (George and Peirce 2025 ; Huang et al. 2022 ; Watson 2024 ). Specific challenges arise with non-explainable AI (NXAI), which creates accountability dilemmas since clinical decisions are not accompanied by transparent reasoning (Wynn 2025 ). Such opacity blurs professional responsibility boundaries and may undermine trust between patients and healthcare providers. The analyzed data also show that Nursing remains the most dominant research area (50%). Interdisciplinary collaboration is increasingly evident through contributions from General Internal Medicine (16.7%), Education , Health Services , and Computer and Information Sciences . This underscores that the integration of AI in nursing is inherently cross-disciplinary—bridging technical, social, and ethical domains. This interconnectedness is also reflected in the bibliographic coupling, where journals such as BMJ Open , Nursing Ethics , and BMC Nursing emerge as primary knowledge platforms, indicating that AI research in nursing is deeply rooted in ethical theory, management, and professional education. Moreover, the findings reveal that algorithmic bias and data protection remain major concerns (Verma et al. 2025 ). The use of non-representative datasets increases the risk of bias and inequitable care delivery, particularly for vulnerable populations (Wang and Xia 2024 ). These implications are critical, as the principle of justice in nursing ethics demands equal access and patient safety. This finding aligns with the keyword cluster trends, where artificial intelligence , nursing ethics , machine learning , and big data frequently co-occur—reinforcing the research orientation toward technological integration and ethical accountability. Concerns also arise regarding the nurse–patient relationship. Paladino ( 2023 ) asserts that technology must not erode the ethical values embedded in the ethics of care , such as presence, empathy, and human connection. Stokes and Palmer ( 2020 ) and Zhu et al. ( 2022 ) also highlight ambiguities in responsibility attribution within clinical automation, while Tabudlo et al. ( 2022 ) found that robot integration often leads to role confusion. Therefore, although AI can streamline clinical workflows, the human relationship remains at the heart of healthcare delivery. In the educational context, the literature emphasizes the urgency of integrating AI and ethics into nursing curricula. Sengul et al. ( 2025 ) stress the importance of developing ethical awareness and critical reflection skills to enable nurses to evaluate and use AI responsibly. This aligns with the dominance of AI literacy and nursing students as recurring keywords in the co-occurrence analysis. The emerging competencies include data literacy, AI output interpretation, and an understanding of legal and ethical contexts (Lattuca et al. 2023 ). From a collaboration standpoint, the author network reveals three main clusters. Michalowski, M.—the most productive author with three publications—leads a cluster focusing on information technology and data analytics. Other clusters, led by Sanna Salantera and Chu, Charlene H., function as regional and interdisciplinary connectors, illustrating the importance of global collaboration in advancing AI-driven nursing practice. Geographically, the United States, China, the United Kingdom, and Australia appear as central nodes within the global research network. In terms of policy, the literature highlights the need for adaptive regulatory mechanisms and well-defined ethical standards to address privacy and accountability issues (Bakošová 2020 ). Bibliometric evidence shows that studies on AI and the General Data Protection Regulation (GDPR) continue to evolve, with publications appearing in reputable journals such as Nursing Ethics and Machine Learning and Knowledge Extraction . International initiatives like the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative further emphasize the urgency of developing global ethical guidelines (Ronquillo et al. 2021 ). Looking forward, a more comprehensive approach is needed to bridge the gap between the benefits of AI and the protection of professional ethics. The concept of Technology-Enhanced Wisdom (TEW), for example, proposes integrating clinical wisdom with AI sophistication to ensure that decision-making remains responsible and patient-centered (Vyas and Gephart 2025 ). Furthermore, long-term research examining the impact of AI on therapeutic relationships and clinical outcomes remains a priority agenda (Bodur et al. 2025 ). In conclusion, the integration of AI into nursing is not merely a technological adoption process but an ethical transformation requiring critical understanding, global collaboration, and a strong commitment to preserving human values. The success of this integration is measured not only by system efficiency but also by its ability to uphold human dignity, professional integrity, and justice in healthcare delivery. Conclusion This bibliometric study demonstrates that research on the ethical dimensions of artificial intelligence (AI) in nursing practice has grown substantially between 2019 and 2025, with the United States, China, and the United Kingdom as the leading contributors. The rapid increase in publications highlights that AI adoption has become a global concern in nursing, encompassing not only technological aspects but also ethical, educational, and governance considerations. The analysis identifies data privacy, algorithmic bias, model transparency, and professional accountability as the primary concerns. Conversely, AI offers significant benefits, including enhanced clinical decision-making, faster patient condition monitoring, and reduced administrative burdens. However, these advancements may risk undermining core aspects of nursing practice, such as empathy, human interaction, and patient dignity. Therefore, AI implementation strategies should prioritize a balance between technological efficiency and humanistic values. Key measures include establishing a comprehensive ethical governance framework, improving AI literacy among nurses, and developing adaptive regulatory policies. Global interdisciplinary collaboration is also essential to strengthen research capacity, technological innovation, and practice guideline development. Overall, the successful integration of AI into nursing will depend on the profession’s ability to adopt technology without compromising autonomy, justice, safety, and the ethics of care—ensuring that AI serves as a partner that enhances, rather than replaces, the human touch in healthcare delivery. References Amiri H, Peiravi S, rezazadeh shojaee S, sara, Rouhparvarzamin M, Nateghi MN, Etemadi MH, ShojaeiBaghini M, Musaie F, Anvari MH, Asadi Anar M (2024) Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Med Educ 24(1):412. https://doi.org/10.1186/s12909-024-05406-1 Arcadi P (2025) Nursing leadership and artificial intelligence ethics: Safeguarding relationships and values. Nurs Ethics. https://doi.org/10.1177/09697330251366599 Badawy W, Helal MMI, Hashim A, Zinhom H, Shaban M (2025a) Ethical boundaries and data-sharing practices in AI‐enhanced nursing: An Arab perspective. Int Nurs Rev 72(1). https://doi.org/10.1111/inr.70013 Badawy W, Shaban M, Zinhom H (2025b) Artificial intelligence in nursing practice and education: A 10-year bibliometric analysis of trends, gaps and emerging frontiers. Nurse Educ Pract 88:104554. https://doi.org/10.1016/j.nepr.2025.104554 Bakošová L (2020) Ethical and Legal Aspects of the Use of Artificial Intelligence in Health and Nursing Care. STUDIA IURIDICA Cassoviensia 8(2). https://doi.org/10.33542/SIC2020-2-01 Bodur G, Cakir H, Turan S, Seren AKH, Goktas P (2025) Artificial intelligence in nursing practice: a qualitative study of nurses’ perspectives on opportunities, challenges, and ethical implications. BMC Nurs 24(1):1263. https://doi.org/10.1186/s12912-025-03775-6 De Gagne JC, Hwang H, Jung D (2024) Cyberethics in nursing education: Ethical implications of artificial intelligence. Nurs Ethics 31(6):1021–1030. https://doi.org/10.1177/09697330231201901 Dornan M (2025) Every nurse an AI nurse: A framework for integrating artificial intelligence across nursing practice, education, research and policy. Digit Health 11. https://doi.org/10.1177/20552076251377939 Dwivedi YK, Kshetri N, Hughes L, Slade EL, Jeyaraj A, Kar AK, Baabdullah AM, Koohang A, Raghavan V, Ahuja M, Albanna H, Albashrawi MA, Al-Busaidi AS, Balakrishnan J, Barlette Y, Basu S, Bose I, Brooks L, Buhalis D, Carter L, Chowdhury S, Crick T, Cunningham SW, Davies GH, Davison RM, Dé R, Dennehy D, Duan Y, Dubey R, Dwivedi R, Edwards JS, Flavián C, Gauld R, Grover V, Hu M-C, Janssen M, Jones P, Junglas I, Khorana S, Kraus S, Larsen KR, Latreille P, Laumer S, Malik FT, Mardani A, Mariani M, Mithas S, Mogaji E, Nord JH, O’Connor S, Okumus F, Pagani M, Pandey N, Papagiannidis S, Pappas IO, Pathak N, Pries-Heje J, Raman R, Rana NP, Rehm S-V, Ribeiro-Navarrete S, Richter A, Rowe F, Sarker S, Stahl BC, Tiwari MK, van der Aalst W, Venkatesh V, Viglia G, Wade M, Walton P, Wirtz J, Wright R (2023) Opinion Paper: So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int J Inf Manag 71:102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 Gallagher A (2024) Artificial intelligence and nursing: The good, the bad and the cautionary. Nurs Ethics 31(6):995–998. https://doi.org/10.1177/09697330241286344 George A, Peirce AG (2025) Artificial Intelligence in Critical Care Nursing: Benefits, Risks, and Ethical Considerations. Crit Care Nurse 45(5):46–52. https://doi.org/10.4037/ccn2025746 Hassanein S, El Arab RA, Abdrbo A, Abu-Mahfouz MS, Gaballah MKF, Seweid MM, Almari M, Alzghoul H (2025) Artificial intelligence in nursing: an integrative review of clinical and operational impacts. Front Digit Health 7. https://doi.org/10.3389/fdgth.2025.1552372 Hu M, Wang Y, Liu Y, Cai B, Kong F, Zheng Q, Zhao D, Gao G, Hui Z (2025) Artificial Intelligence in Nursing Decision-Making: A Bibliometric Analysis of Trends and Impacts. Nurs Rep 15(6):198. https://doi.org/10.3390/nursrep15060198 Huang K, Jiao Z, Cai Y, Zhong Z (2022) Artificial intelligence-based intelligent surveillance for reducing nurses’ working hours in nurse–patient interaction: A two‐wave study. J Nurs Adm Manag 30(8):3817–3826. https://doi.org/10.1111/jonm.13787 Kwak Y, Ahn J-W, Seo YH (2022) Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions. BMC Nurs 21(1):267. https://doi.org/10.1186/s12912-022-01048-0 Lattuca M, Maratta D, Beffert U, Chevrier A, Winer L (2023) Healthcare AI: A Revised Quebec Framework for Nursing Education. Qual Advancement Nurs Educ - Avancées En Formation Infirmière 9(3). https://doi.org/10.17483/2368-6669.1408 Mohammad Amini M, Jesus M, Fanaei Sheikholeslami D, Alves P, Hassanzadeh Benam A, Hariri F (2023) Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate. Mach Learn Knowl Extr 5(3):1023–1035. https://doi.org/10.3390/make5030053 Nashwan AJ, Abujaber A, Ahmed SK (2024) Charting the Future: The Role of AI in Transforming Nursing Documentation. Cureus . https://doi.org/10.7759/cureus.57304 Paladino MS (2023) An ethical pathway for the use of Artificial Intelligence in Medicine and Nursing Care. Med e Morale 72(2):121–139. https://doi.org/10.4081/mem.2023.1231 Ronquillo CE, Peltonen L, Pruinelli L, Chu CH, Bakken S, Beduschi A, Cato K, Hardiker N, Junger A, Michalowski M, Nyrup R, Rahimi S, Reed DN, Salakoski T, Salanterä S, Walton N, Weber P, Wiegand T, Topaz M (2021) Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs 77(9):3707–3717. https://doi.org/10.1111/jan.14855 Sengul T, Sariköse S, Gul A (2025) Ethical decision-making and artificial intelligence in nursing education: An integrative review. Nurs Ethics. https://doi.org/10.1177/09697330251366600 Soriano GP, Yasuhara Y, Ito H, Matsumoto K, Osaka K, Kai Y, Locsin R, Schoenhofer S, Tanioka T (2022) Robots and Robotics in Nursing. Healthcare 10(8), 1571. https://doi.org/10.3390/healthcare10081571 Stokes F, Palmer A (2020) Artificial Intelligence and Robotics in Nursing: Ethics of Caring as a Guide to Dividing Tasks Between AI and Humans. Nurs Philos 21(4). https://doi.org/10.1111/nup.12306 Tabudlo J, Kuan L, Garma PF (2022) Can nurses in clinical practice ascribe responsibility to intelligent robots? Nurs Ethics 29(6):1457–1465. https://doi.org/10.1177/09697330221090591 Verma R, Macaskill F, Kim A, Raison N, Dasgupta P (2025) Ethical aspects of artificial intelligence: what urologists need to know. Curr Opin Urol 35(3):224–229. https://doi.org/10.1097/MOU.0000000000001278 Vyas P, Gephart S (2025) Proposing Technology-Enhanced Wisdom. Adv Nurs Sci. https://doi.org/10.1097/ANS.0000000000000585 Wang S, Xia Z (2024) Development Trends of Artificial Intelligence Ethics. Open Access J Educ Lang Stud 2(1). https://doi.org/10.19080/OAJELS.2024.02.555576 Watson AL (2024) Ethical considerations for artificial intelligence use in nursing informatics. Nurs Ethics 31(6):1031–1040. https://doi.org/10.1177/09697330241230515 Wynn M (2025) The ethics of non-explainable artificial intelligence: an overview for clinical nurses. Br J Nurs 34(5):294–297. https://doi.org/10.12968/bjon.2024.0394 Yelne S, Chaudhary M, Dod K, Sayyad A, Sharma R (2023) Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare. Cureus . https://doi.org/10.7759/cureus.49252 Zhu J, Shi K, Yang C, Niu Y, Zeng Y, Zhang N, Liu T, Chu CH (2022) Ethical issues of smart home-based elderly care: A scoping review. J Nurs Adm Manag 30(8):3686–3699. https://doi.org/10.1111/jonm.13521 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8033889","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":540134130,"identity":"215147bd-2605-43af-b97b-d09087769fca","order_by":0,"name":"Fandro Armando Tasijawa","email":"","orcid":"https://orcid.org/0000-0002-0444-3840","institution":"Doctoral Nursing Students, Faculty of Nursing, Universitas Airlangga","correspondingAuthor":false,"prefix":"","firstName":"Fandro","middleName":"Armando","lastName":"Tasijawa","suffix":""},{"id":540137150,"identity":"9bd811e5-97e1-4b14-ab8a-e375176d727e","order_by":1,"name":"Widya 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Scientific production by country\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8033889/v1/786f14a5793eb294fdbaa4bc.png"},{"id":95287146,"identity":"a61d8490-64f8-4bd0-89c4-d08a7fe6a7f9","added_by":"auto","created_at":"2025-11-06 10:09:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":650933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5\u003c/strong\u003e. Keyword co-occurrence clusters\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8033889/v1/fb432f72fc0c55ee40efaa20.png"},{"id":95316204,"identity":"28231c03-883a-44b5-aa60-fc903eb66429","added_by":"auto","created_at":"2025-11-06 15:57:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3006206,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8033889/v1/9f27f814-e8b3-4507-90a6-02e6e56c5cf1.pdf"},{"id":95287135,"identity":"02bc1afd-8eeb-4839-822a-d291e1b6e09f","added_by":"auto","created_at":"2025-11-06 10:09:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38173,"visible":true,"origin":"","legend":"\u003cp\u003eTable\u003c/p\u003e","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8033889/v1/55e917482b4a99a844f731ed.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExploring the Ethical Landscape of Artificial Intelligence in Nursing Practice: A Bibliometric Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial Intelligence (AI) has emerged as a transformative force in global healthcare, offering immense potential to enhance diagnostic accuracy, accelerate clinical decision-making, and optimize the efficiency of nursing service systems (Badawy et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In nursing practice, AI functions not only as a clinical support tool through predictive analytics and decision support systems but also as an innovative mechanism in nursing education, hospital management, and data-driven research (Badawy et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). However, alongside these opportunities lie a range of complex ethical challenges related to data privacy, algorithmic fairness, transparency, patient autonomy, and the professional accountability of nurses (Gallagher \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOver the past decade, the adoption of AI in nursing has increased exponentially. Bibliometric mapping has revealed a significant surge in global publications on AI and nursing between 2014 and 2024, with the United States, China, and Canada emerging as the main contributors (Badawy et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). This growth reflects a paradigm shift from a focus on nursing informatics toward broader concerns, including ethics, digital literacy, and the governance of technology in nursing practice and education. Furthermore, global bibliometric analyses of \u0026ldquo;AI in nursing decision-making\u0026rdquo; show an annual increase of 7.6%, underscoring the growing attention toward AI integration in clinical practice and patient safety (Hu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNevertheless, the implementation of AI in nursing is not without ethical risks. First, issues of data privacy and governance have come to the forefront, as AI systems often rely on large volumes of patient data without adequate consent mechanisms (Mohammad Amini et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, accountability and transparency remain ambiguous\u0026mdash;particularly regarding who is responsible for clinical decisions generated by AI-based systems (Wynn \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Third, algorithmic bias and fairness raise concerns that AI may reinforce existing healthcare disparities, particularly among vulnerable populations (Hassanein et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Equally important, AI has the potential to erode core nursing values such as empathy, compassion, and human connection\u0026mdash;the essence of nursing care (Arcadi \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gallagher \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn nursing education, there is an urgent need to equip nursing students and practitioners with sufficient ethical digital literacy to critically assess, utilize, and monitor AI systems responsibly (De Gagne et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sengul et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Correspondingly, nursing leadership is urged to develop ethical governance frameworks that ensure AI implementation remains aligned with humanistic values and social justice (Dornan \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven these complexities, a comprehensive approach is required to map the global ethical landscape of AI in nursing. The bibliometric method provides a suitable means of conducting this analysis, as it allows for quantitative examination of publication patterns, scientific collaborations, and the evolution of research themes (Badawy et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, this study aims to explore the ethical landscape of AI implementation in nursing practice by analyzing publication trends, major research domains, and the scholarly networks shaping this field. The findings are expected to provide a conceptual foundation for the development of ethical policies, education, and nursing practices in the digital era.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThis study aimed to comprehensively map the development, scientific collaboration, and thematic focus of research related to Artificial Intelligence (AI) in nursing practice, particularly from the perspective of nursing ethics. A bibliometric approach was employed, allowing for quantitative analysis of scientific publications to identify trends, collaboration patterns, and the emerging knowledge networks within this field.\u003c/p\u003e\u003cp\u003eThe bibliographic data for this study were obtained from the Web of Science Core Collection (WoSCC), which was selected as the most suitable database for bibliometric research, particularly for conducting co-citation, co-authorship, and keyword co-occurrence analyses. Data retrieval was performed on October 30, 2025, using the advanced search function in WoS with the following search query: TS = (Artificial Intelligence) AND TS = (Ethics) AND TS = (Nursing).\u003c/p\u003e\u003cp\u003eThe initial search yielded 236 articles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To ensure data accuracy and reliability, the search process was conducted independently by two researchers, verified by two additional researchers, and finally validated by an independent reviewer. All researchers involved in data searching, screening, reviewing, and extraction worked independently to minimize subjective bias.\u003c/p\u003e\u003cp\u003eThe inclusion criteria consisted of research articles and review papers published between 2019 and 2025, written in English, and indexed in the Science Citation Index (SCI), Social Sciences Citation Index (SSCI), or Social Sciences Citation Index-Expanded (SSCI-E). Publications such as conference proceedings, editorials, book chapters, and early access articles were excluded from the analysis. Each retrieved article was manually screened to confirm its relevance to the topic of ethics and artificial intelligence in nursing, while irrelevant papers were excluded. After the multi-step screening process, 68 articles met the inclusion criteria and were retained for final analysis. All bibliographic data were exported in the \u0026ldquo;full records with cited references\u0026rdquo; format for subsequent analysis using VOSviewer.\u003c/p\u003e\u003cp\u003eBibliometric analysis and data visualization were conducted using three main tools: the Web of Science Analysis Tool, VOSviewer (version 1.6.20), and Microsoft Excel 2021. These tools were applied in an integrated manner to provide a comprehensive overview of publication trends, collaborative relationships among authors and institutions, and the conceptual structure of research on AI ethics in nursing practice.\u003c/p\u003e\u003cp\u003eFirst, the Web of Science Analysis Tool was used to perform descriptive analyses of the data, including the identification of the most productive authors, the countries with the largest contributions, leading institutions, key journals, and the annual growth of publications. The results were visualized through graphs and diagrams to provide a quantitative understanding of research dynamics and growth patterns.\u003c/p\u003e\u003cp\u003eSecond, VOSviewer was employed to construct network-based bibliometric maps (network visualizations). This tool was used to map international collaboration networks, co-authorship relationships among researchers and institutions, and keyword co-occurrence patterns that illustrate the interconnections between ethics, nursing, and artificial intelligence. In VOSviewer visualizations, colors represent thematic clusters, connecting lines indicate the strength of relationships (link strength) among elements, and circle sizes depict the frequency or contribution level of each element within the research network.\u003c/p\u003e\u003cp\u003eThird, Microsoft Excel 2021 was utilized for supplementary descriptive analyses and the creation of supporting visualizations such as frequency tables, bar charts, and publication trend graphs. Data exported from WoS were processed in Excel to illustrate publication dynamics, collaboration patterns, and keyword evolution over time. The visual outputs from Excel also served as the foundation for further analysis in VOSviewer, resulting in a more structured and in-depth knowledge map.\u003c/p\u003e\u003cp\u003eOverall, the combination of these three analytical tools produced a comprehensive and integrated methodological framework for depicting the research landscape of AI ethics in nursing practice. This approach enabled the in-depth identification of scientific collaboration patterns, key thematic trends, and the developmental trajectory of this rapidly expanding field.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eAnnual Distribution of Publications\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the annual distribution of publications and citation counts from 2019 to 2025. The data reveal a significant upward trajectory in publication numbers over time, reflecting the rapidly growing research interest in this topic. In 2019, there was only one article (1.47%) with 29 citations, marking the initial phase of exploration in this field. In 2020, the number of publications increased to two articles (2.94%) with 118 citations, followed by another two articles (2.94%) in 2021, which received 182 citations\u0026mdash;indicating growing scholarly recognition and engagement.\u003c/p\u003e\u003cp\u003eThe expansion became more pronounced in 2022, with five articles (7.35%) and 210 citations, representing a phase of steady growth. A substantial surge occurred in 2023, with seven articles (10.29%) and a total of 1,897 citations, demonstrating the heightened academic attention toward this research area. The peak of productivity was observed in 2024, with 16 articles (23.52%) receiving 219 citations. Meanwhile, 2025 recorded the sharpest rise in publication numbers, totaling 35 articles (51.47%), although the citation count (26) remained relatively low\u0026mdash;likely because most publications from that year were still recent and had not yet accumulated extensive citations.\u003c/p\u003e\u003cp\u003eOverall, the findings indicate an exponential growth trend in publications over the past seven years, underscoring the increasing global relevance and scholarly interest in the ethical dimensions of Artificial Intelligence in nursing practice.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual trends in publication output and citation frequency between 2019 and 2025\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Articles\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal number of citations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% of\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCitation Trends\u003c/h3\u003e\n\u003cp\u003eThe top ten articles in this analysis reveal a strong and consistent research trend concerning the application and ethical implications of Artificial Intelligence (AI) in nursing. The most influential publication, titled \u003cem\u003e\u0026ldquo;So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy\u0026rdquo;\u003c/em\u003e by Dwivedi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) published in the \u003cem\u003eInternational Journal of Information Management\u003c/em\u003e, received a total of 1,746 citations, averaging 582 citations per year. This underscores the considerable scholarly attention devoted to generative AI and its far-reaching implications for research and policy development.\u003c/p\u003e\u003cp\u003eThe second most cited study, conducted by Ronquillo et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), focuses on identifying priorities and opportunities for AI integration within nursing, followed by the work of Kwak et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which investigates the effects of ethical awareness, attitudes, and self-efficacy on nursing students\u0026rsquo; behavioral intentions. Other notable contributions, such as those by Stokes and Palmer (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), examine the ethical dimensions of task-sharing between humans and AI, while Zhu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) address ethical concerns in elderly care supported by smart home technologies.\u003c/p\u003e\u003cp\u003ePublications appearing in high-impact journals such as the \u003cem\u003eJournal of Advanced Nursing\u003c/em\u003e, \u003cem\u003eBMC Nursing\u003c/em\u003e, \u003cem\u003eNursing Philosophy\u003c/em\u003e, and \u003cem\u003eNursing Ethics\u003c/em\u003e demonstrate that AI in nursing has emerged as a multidisciplinary domain encompassing ethical, educational, and clinical perspectives. Overall, the high citation frequency of recent publications (2023\u0026ndash;2024) indicates that this topic is evolving rapidly, with research trends increasingly emphasizing the integration of technology, human values, and professional accountability in navigating the digital transformation of the nursing profession.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop 10 cited studies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTitle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAuthors and Year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage citation per year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal Citation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSo what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Dwivedi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInternational Journal of Information Management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArtificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Ronquillo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJournal of Advanced Nursing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfluence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students' behavioral intentions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Kwak et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBMC Nursing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArtificial Intelligence and Robotics in Nursing: Ethics of Caring as a Guide to Dividing Tasks Between AI and Humans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Stokes and Palmer \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNursing Philosophy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHarnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Yelne et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCureus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEthical issues of smart home-based elderly care: A scoping review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Zhu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJournal of Nursing Management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRobots and Robotics in Nursing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Soriano et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealthcare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Amiri et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBMC Medical Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArtificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Mohammad Amini et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMachine Learning and Knowledge Extraction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyberethics in nursing education: Ethical implications of artificial intelligence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(De Gagne et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNursing Ethics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eResearch Area\u003c/h3\u003e\n\u003cp\u003eThis research area analysis reveals that contributions to this theme are predominantly concentrated in the field of Nursing, which accounts for the largest share\u0026mdash;50% of total publications. This finding underscores that studies on the ethics of Artificial Intelligence (AI) are primarily grounded in the context of nursing practice. The scope of research also overlaps with General Internal Medicine (16.7%), indicating that the application of AI in general healthcare settings likewise raises relevant ethical concerns.\u003c/p\u003e\u003cp\u003eIn addition, publications in Education Educational Research, Health Care Sciences Services, and Social Sciences Other Topics each account for 8.8%, suggesting that ethical studies on AI extend beyond clinical aspects to include dimensions of education, service governance, and social perspectives. Further contributions are also evident in Computer Science and Medical Informatics (each 5.8%), reflecting the involvement of technological disciplines in deepening the understanding of AI\u0026rsquo;s ethical dynamics within the nursing sector, including challenges related to adoption and system integration.\u003c/p\u003e\u003cp\u003eMeanwhile, research within Public Environmental Occupational Health (4.4%), Business Economics (2.9%), and Area Studies (1.4%) demonstrates broader interdisciplinary exploration, highlighting that the discourse on AI ethics in nursing has evolved into a multidisciplinary field encompassing wider systemic implications.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop 10 research areas in AI ethics in nursing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of publications\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNursing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral Internal Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Educational Research\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Care Sciences Services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Science Other Topics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComputer Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Informatics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic Environmental Occupational Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusiness Economics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea Studies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMost Prolific Journals\u003c/h3\u003e\n\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe bibliographic analysis highlights the journals most frequently cited in studies related to AI and nursing. The visualization shows that \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003eNursing Ethics\u003c/em\u003e, and \u003cem\u003eBMC Nursing\u003c/em\u003e have the largest nodes, indicating their roles as the most influential and frequently referenced journals in the literature. Larger node sizes represent higher citation frequencies, while the thickness of the connecting lines between nodes reflects strong referential relationships among these journals.\u003c/p\u003e\u003cp\u003eCluster analysis reveals several significant groupings. The green cluster emphasizes journals such as \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003eJournal of Nursing Management\u003c/em\u003e, and \u003cem\u003eJournal of Advanced Nursing\u003c/em\u003e, which generally discuss ethics, management, and clinical practice within the context of AI implementation. This cluster represents a research direction that underscores the importance of ethical governance and technological integration within nursing care systems.\u003c/p\u003e\u003cp\u003eThe red cluster includes journals such as \u003cem\u003eBMC Nursing\u003c/em\u003e, \u003cem\u003eSage Open Nursing\u003c/em\u003e, \u003cem\u003eCreative Nursing\u003c/em\u003e, and \u003cem\u003eAdvances in Skin \u0026amp; Wound Care\u003c/em\u003e. Its primary focus lies on education, innovation, and the development of AI-based learning models in nursing.\u003c/p\u003e\u003cp\u003eThe yellow cluster comprises journals such as \u003cem\u003eBMC Medical Education\u003c/em\u003e and \u003cem\u003eFrontiers in Education\u003c/em\u003e, which highlight the role of AI in health education and nursing training. Meanwhile, the blue and purple clusters\u0026mdash;featuring \u003cem\u003eFrontiers in Digital Health\u003c/em\u003e and \u003cem\u003eACM Transactions on Computer-Human Interaction\u003c/em\u003e\u0026mdash;illustrate the intersection between nursing and computer science, revealing a growing trend toward interdisciplinary research that bridges digital technology, human\u0026ndash;computer interaction, and healthcare.\u003c/p\u003e\u003cp\u003eOverall, these citation patterns demonstrate that AI research in nursing is not solely driven by technological innovation but is also deeply grounded in ethical discourse and professional education literature. Consequently, research developments in this field increasingly emphasize the responsible and human-centered integration of technology into nursing practice and healthcare education.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMost Prolific Authors\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a list of the most productive authors in the analyzed research field, including their affiliated countries, home institutions, total number of published articles, and their respective percentages of total publications. Based on the data, it is evident that Michalowski M from the University of Minnesota, United States, ranks first with a total of three articles (4.41%), indicating the most substantial contribution among all identified authors.\u003c/p\u003e\u003cp\u003eSeveral other authors show comparable productivity levels, each having published two articles (2.94%). They represent diverse countries and reputable institutions, reflecting the global distribution of research in this area. For example, Alhur AA from the University of Hail (Saudi Arabia), Chu CH from the University of Toronto (Canada), Dillard-Wright J from the University of Massachusetts Amherst (USA), and El Arab RA from Almoosa College of Health Sciences (Saudi Arabia) are among them.\u003c/p\u003e\u003cp\u003eNotably, contributions from Europe and Scandinavia are also significant, represented by Peltonen LM from the University of Eastern Finland (Finland) and Sagbakken M from Oslo Metropolitan University (Norway). From other regions, Shaban M from the University of Leicester (United Kingdom) and Topaz M from Columbia University (USA) demonstrate valuable contributions to the advancement of this research theme.\u003c/p\u003e\u003cp\u003eOverall, the findings indicate that research in this field is multinational and collaborative, with the United States emerging as the most dominant country in terms of both author representation and institutional affiliation. Although the number of publications per author remains relatively modest (two to three articles), this pattern suggests that the research area is still developing and is characterized by cross-institutional and international collaboration.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLeading 10 authors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName of the author\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry of affiation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAffiliated institution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal number of published articles\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMichalowski M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUniversity of Minnesota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlhur AA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUniversity of Hail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChu CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCanada\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUniversity of Toronto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDillard-Wright J\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUniversity of Massachusetts Amherst\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEl Arab RA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlmoosa College of Health Sciences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeltonen LM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUniversity of Eastern Finland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePruinelli L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUniversity of Florida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSagbakken M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOslo Metropolitan University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShaban M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnited Kingdom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUniversity of Leicester\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopaz M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnited States\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eColumbia University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCollaborations Among Authors\u003c/h3\u003e\n\u003cp\u003eThe author collaboration network map, involving 401 researchers, reveals three main clusters that interact based on the strength of their collaborative relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The first cluster (blue) is led by Michalowski, Martin, with key collaborators such as Topaz, Maxim, Pruinelli, Lisiane, and Peltonen, Laura-Maria, focusing on the application of information technology and data analytics in nursing practice.\u003c/p\u003e\u003cp\u003eThe second cluster (red) is centered around Sanna Salantera and Ana Beduschi, who frequently collaborate with researchers including Nancy Walton, Suzanne Bakken, and Nicholas Hardiker, emphasizing ethical issues, innovations in information systems, and the implementation of digital health in nursing.\u003c/p\u003e\u003cp\u003eMeanwhile, the third cluster (green) is led by Chu, Charlene H., who acts as a key link between European and Asian research networks through collaborations with Zeng, Yingchun, Yang, Chengyue, Liu, Tao, and Niu, Yanping. The dominant research theme within this cluster focuses on the adoption of artificial intelligence and digital technologies in nursing care.\u003c/p\u003e\u003cp\u003eThe central position of Chu, Charlene H., connecting multiple clusters, underscores the strength of international collaboration networks and highlights the crucial role of cross-continental researchers in facilitating knowledge exchange and advancing innovation in the field of Artificial Intelligence in Nursing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMost Prolific Countries\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the collaborative relationships among countries. The first cluster (red) is led by the United States (USA), identified as the most productive and influential country, showing strong collaborations with Saudi Arabia, Canada, Turkey, and Japan. This cluster reflects research dominance focused on technological innovation, the implementation of intelligent systems, and AI-based nursing education.\u003c/p\u003e\u003cp\u003eMeanwhile, the second cluster (green) comprises European and Asia-Pacific countries, with China (People\u0026rsquo;s Republic of China), the United Kingdom, Australia, and Germany serving as major research hubs. This cluster demonstrates extensive collaboration with Singapore, India, the Netherlands, and Italy, indicating strong integration of AI research across these regions. The interconnections between the two clusters reveal an extensive global research network, where the United States and China function as key nodes facilitating cross-continental collaborations. Overall, the map underscores that AI research in nursing is international and multidisciplinary in scope, with major scientific power centers distributed across North America, Europe, and Asia.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eKeywords\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe visualization analysis of keywords using VOSviewer reveals a complex yet interconnected thematic structure, illustrating the direction and focus of research related to artificial intelligence (AI) in the field of nursing. The largest node on the map indicates that the term \u003cem\u003e\u0026ldquo;artificial intelligence\u0026rdquo;\u003c/em\u003e serves as the central hub within the network, signifying that this topic is the dominant theme and focal point in the analyzed literature. Based on the clustering results, four major groups were identified, each reflecting distinct research trends.\u003c/p\u003e\u003cp\u003eThe first cluster, marked in yellow, represents the connection between artificial intelligence, data technology, and professional ethics. Keywords such as \u003cem\u003emachine learning\u003c/em\u003e, \u003cem\u003ebig data\u003c/em\u003e, \u003cem\u003enursing ethics\u003c/em\u003e, and \u003cem\u003ehealth care\u003c/em\u003e suggest that research in this group focuses on the application of advanced analytical technologies and moral issues within healthcare contexts. This cluster reflects the nursing profession\u0026rsquo;s transition toward data-driven practice and ethical accountability in the use of AI.\u003c/p\u003e\u003cp\u003eThe second cluster, shown in red, centers on \u003cem\u003eethics\u003c/em\u003e, \u003cem\u003etechnology\u003c/em\u003e, and \u003cem\u003edecision-making\u003c/em\u003e, encompassing keywords such as \u003cem\u003eChatGPT\u003c/em\u003e, \u003cem\u003egenerative AI\u003c/em\u003e, and \u003cem\u003eanxiety\u003c/em\u003e. This theme highlights emerging discourses on the ethics of generative technologies\u0026mdash;particularly ChatGPT\u0026mdash;in clinical decision-making and nursing education. The association with \u003cem\u003eanxiety\u003c/em\u003e indicates growing concern about the psychological impacts and apprehension among healthcare workers regarding automation and artificial intelligence.\u003c/p\u003e\u003cp\u003eThe green cluster represents more practical themes related to \u003cem\u003enursing\u003c/em\u003e, \u003cem\u003erobots\u003c/em\u003e, \u003cem\u003ecare\u003c/em\u003e, and \u003cem\u003estudents\u003c/em\u003e. This group focuses on the implementation of robotics and AI-based technologies to enhance the quality of nursing services and improve learning within health education institutions. Meanwhile, the blue cluster reflects a research orientation emphasizing \u003cem\u003eAI literacy\u003c/em\u003e, \u003cem\u003enursing students\u003c/em\u003e, and \u003cem\u003eclinical decision support\u003c/em\u003e, underscoring the importance of digital literacy and the readiness of nursing professionals to engage with AI integration in practice.\u003c/p\u003e\u003cp\u003eOverall, this visualization demonstrates that research trends in AI and nursing revolve around three major axes: the integration of AI technology into education and clinical practice, the exploration of its ethical and emotional dimensions, and the development of AI literacy among nursing students and professionals. Thus, artificial intelligence emerges not merely as a technological subject but also as a social and ethical phenomenon that profoundly influences the transformation of the nursing discipline.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe integration of artificial intelligence (AI) into nursing practice presents a paradox between its immense potential benefits and its complex ethical challenges. Bibliometric evidence indicates an exponential growth in research interest on this topic. Publications have increased significantly from just one article in 2019 to 35 articles in 2025, reflecting a sharp rise in academic attention toward the ethical dimensions of AI use in nursing. The largest citation spike occurred in 2023, with 1,897 citations, underscoring the high relevance of this issue within scientific and interdisciplinary communities.\u003c/p\u003e\u003cp\u003eThe literature highlights that AI can enhance patient monitoring, support clinical reasoning, and reduce nurses\u0026rsquo; administrative burdens (Nashwan et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Watson \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The bibliometric findings reinforce this perspective\u0026mdash;the most influential article by Dwivedi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) underscores the vast opportunities of generative AI for practice, research, and policy, receiving 1,746 citations, the highest within the analyzed dataset. Several other studies similarly identify ethical concerns, user readiness, and the role of AI in clinical decision-making as central themes in the global discourse(Kwak et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ronquillo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stokes and Palmer \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, these developments raise fundamental questions about how professional ethics can be upheld in an increasingly sophisticated technological ecosystem. The ethical principles of autonomy, beneficence, non-maleficence, and justice remain the core normative framework guiding AI implementation (George and Peirce \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Watson \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specific challenges arise with non-explainable AI (NXAI), which creates accountability dilemmas since clinical decisions are not accompanied by transparent reasoning (Wynn \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such opacity blurs professional responsibility boundaries and may undermine trust between patients and healthcare providers.\u003c/p\u003e\u003cp\u003eThe analyzed data also show that \u003cem\u003eNursing\u003c/em\u003e remains the most dominant research area (50%). Interdisciplinary collaboration is increasingly evident through contributions from \u003cem\u003eGeneral Internal Medicine\u003c/em\u003e (16.7%), \u003cem\u003eEducation\u003c/em\u003e, \u003cem\u003eHealth Services\u003c/em\u003e, and \u003cem\u003eComputer and Information Sciences\u003c/em\u003e. This underscores that the integration of AI in nursing is inherently cross-disciplinary\u0026mdash;bridging technical, social, and ethical domains. This interconnectedness is also reflected in the bibliographic coupling, where journals such as \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003eNursing Ethics\u003c/em\u003e, and \u003cem\u003eBMC Nursing\u003c/em\u003e emerge as primary knowledge platforms, indicating that AI research in nursing is deeply rooted in ethical theory, management, and professional education.\u003c/p\u003e\u003cp\u003eMoreover, the findings reveal that algorithmic bias and data protection remain major concerns (Verma et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The use of non-representative datasets increases the risk of bias and inequitable care delivery, particularly for vulnerable populations (Wang and Xia \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These implications are critical, as the principle of \u003cem\u003ejustice\u003c/em\u003e in nursing ethics demands equal access and patient safety. This finding aligns with the keyword cluster trends, where \u003cem\u003eartificial intelligence\u003c/em\u003e, \u003cem\u003enursing ethics\u003c/em\u003e, \u003cem\u003emachine learning\u003c/em\u003e, and \u003cem\u003ebig data\u003c/em\u003e frequently co-occur\u0026mdash;reinforcing the research orientation toward technological integration and ethical accountability.\u003c/p\u003e\u003cp\u003eConcerns also arise regarding the nurse\u0026ndash;patient relationship. Paladino (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) asserts that technology must not erode the ethical values embedded in the \u003cem\u003eethics of care\u003c/em\u003e, such as presence, empathy, and human connection. Stokes and Palmer (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Zhu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also highlight ambiguities in responsibility attribution within clinical automation, while Tabudlo et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that robot integration often leads to role confusion. Therefore, although AI can streamline clinical workflows, the human relationship remains at the heart of healthcare delivery.\u003c/p\u003e\u003cp\u003eIn the educational context, the literature emphasizes the urgency of integrating AI and ethics into nursing curricula. Sengul et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) stress the importance of developing ethical awareness and critical reflection skills to enable nurses to evaluate and use AI responsibly. This aligns with the dominance of \u003cem\u003eAI literacy\u003c/em\u003e and \u003cem\u003enursing students\u003c/em\u003e as recurring keywords in the co-occurrence analysis. The emerging competencies include data literacy, AI output interpretation, and an understanding of legal and ethical contexts (Lattuca et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom a collaboration standpoint, the author network reveals three main clusters. Michalowski, M.\u0026mdash;the most productive author with three publications\u0026mdash;leads a cluster focusing on information technology and data analytics. Other clusters, led by Sanna Salantera and Chu, Charlene H., function as regional and interdisciplinary connectors, illustrating the importance of global collaboration in advancing AI-driven nursing practice. Geographically, the United States, China, the United Kingdom, and Australia appear as central nodes within the global research network.\u003c/p\u003e\u003cp\u003eIn terms of policy, the literature highlights the need for adaptive regulatory mechanisms and well-defined ethical standards to address privacy and accountability issues (Bakošov\u0026aacute; \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Bibliometric evidence shows that studies on AI and the General Data Protection Regulation (GDPR) continue to evolve, with publications appearing in reputable journals such as \u003cem\u003eNursing Ethics\u003c/em\u003e and \u003cem\u003eMachine Learning and Knowledge Extraction\u003c/em\u003e. International initiatives like the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative further emphasize the urgency of developing global ethical guidelines (Ronquillo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLooking forward, a more comprehensive approach is needed to bridge the gap between the benefits of AI and the protection of professional ethics. The concept of \u003cem\u003eTechnology-Enhanced Wisdom\u003c/em\u003e (TEW), for example, proposes integrating clinical wisdom with AI sophistication to ensure that decision-making remains responsible and patient-centered (Vyas and Gephart \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, long-term research examining the impact of AI on therapeutic relationships and clinical outcomes remains a priority agenda (Bodur et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn conclusion, the integration of AI into nursing is not merely a technological adoption process but an ethical transformation requiring critical understanding, global collaboration, and a strong commitment to preserving human values. The success of this integration is measured not only by system efficiency but also by its ability to uphold human dignity, professional integrity, and justice in healthcare delivery.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis bibliometric study demonstrates that research on the ethical dimensions of artificial intelligence (AI) in nursing practice has grown substantially between 2019 and 2025, with the United States, China, and the United Kingdom as the leading contributors. The rapid increase in publications highlights that AI adoption has become a global concern in nursing, encompassing not only technological aspects but also ethical, educational, and governance considerations.\u003c/p\u003e\u003cp\u003eThe analysis identifies data privacy, algorithmic bias, model transparency, and professional accountability as the primary concerns. Conversely, AI offers significant benefits, including enhanced clinical decision-making, faster patient condition monitoring, and reduced administrative burdens. However, these advancements may risk undermining core aspects of nursing practice, such as empathy, human interaction, and patient dignity.\u003c/p\u003e\u003cp\u003eTherefore, AI implementation strategies should prioritize a balance between technological efficiency and humanistic values. Key measures include establishing a comprehensive ethical governance framework, improving AI literacy among nurses, and developing adaptive regulatory policies. Global interdisciplinary collaboration is also essential to strengthen research capacity, technological innovation, and practice guideline development.\u003c/p\u003e\u003cp\u003eOverall, the successful integration of AI into nursing will depend on the profession\u0026rsquo;s ability to adopt technology without compromising autonomy, justice, safety, and the ethics of care\u0026mdash;ensuring that AI serves as a partner that enhances, rather than replaces, the human touch in healthcare delivery.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmiri H, Peiravi S, rezazadeh shojaee S, sara, Rouhparvarzamin M, Nateghi MN, Etemadi MH, ShojaeiBaghini M, Musaie F, Anvari MH, Asadi Anar M (2024) Medical, dental, and nursing students\u0026rsquo; attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Med Educ 24(1):412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12909-024-05406-1\u003c/span\u003e\u003cspan address=\"10.1186/s12909-024-05406-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArcadi P (2025) Nursing leadership and artificial intelligence ethics: Safeguarding relationships and values. Nurs Ethics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/09697330251366599\u003c/span\u003e\u003cspan address=\"10.1177/09697330251366599\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBadawy W, Helal MMI, Hashim A, Zinhom H, Shaban M (2025a) Ethical boundaries and data-sharing practices in AI‐enhanced nursing: An Arab perspective. Int Nurs Rev 72(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/inr.70013\u003c/span\u003e\u003cspan address=\"10.1111/inr.70013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBadawy W, Shaban M, Zinhom H (2025b) Artificial intelligence in nursing practice and education: A 10-year bibliometric analysis of trends, gaps and emerging frontiers. Nurse Educ Pract 88:104554. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nepr.2025.104554\u003c/span\u003e\u003cspan address=\"10.1016/j.nepr.2025.104554\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBakošov\u0026aacute; L (2020) Ethical and Legal Aspects of the Use of Artificial Intelligence in Health and Nursing Care. STUDIA IURIDICA Cassoviensia 8(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.33542/SIC2020-2-01\u003c/span\u003e\u003cspan address=\"10.33542/SIC2020-2-01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBodur G, Cakir H, Turan S, Seren AKH, Goktas P (2025) Artificial intelligence in nursing practice: a qualitative study of nurses\u0026rsquo; perspectives on opportunities, challenges, and ethical implications. BMC Nurs 24(1):1263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12912-025-03775-6\u003c/span\u003e\u003cspan address=\"10.1186/s12912-025-03775-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Gagne JC, Hwang H, Jung D (2024) Cyberethics in nursing education: Ethical implications of artificial intelligence. Nurs Ethics 31(6):1021\u0026ndash;1030. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/09697330231201901\u003c/span\u003e\u003cspan address=\"10.1177/09697330231201901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDornan M (2025) Every nurse an AI nurse: A framework for integrating artificial intelligence across nursing practice, education, research and policy. Digit Health 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20552076251377939\u003c/span\u003e\u003cspan address=\"10.1177/20552076251377939\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDwivedi YK, Kshetri N, Hughes L, Slade EL, Jeyaraj A, Kar AK, Baabdullah AM, Koohang A, Raghavan V, Ahuja M, Albanna H, Albashrawi MA, Al-Busaidi AS, Balakrishnan J, Barlette Y, Basu S, Bose I, Brooks L, Buhalis D, Carter L, Chowdhury S, Crick T, Cunningham SW, Davies GH, Davison RM, D\u0026eacute; R, Dennehy D, Duan Y, Dubey R, Dwivedi R, Edwards JS, Flavi\u0026aacute;n C, Gauld R, Grover V, Hu M-C, Janssen M, Jones P, Junglas I, Khorana S, Kraus S, Larsen KR, Latreille P, Laumer S, Malik FT, Mardani A, Mariani M, Mithas S, Mogaji E, Nord JH, O\u0026rsquo;Connor S, Okumus F, Pagani M, Pandey N, Papagiannidis S, Pappas IO, Pathak N, Pries-Heje J, Raman R, Rana NP, Rehm S-V, Ribeiro-Navarrete S, Richter A, Rowe F, Sarker S, Stahl BC, Tiwari MK, van der Aalst W, Venkatesh V, Viglia G, Wade M, Walton P, Wirtz J, Wright R (2023) Opinion Paper: So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int J Inf Manag 71:102642. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2023.102642\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2023.102642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGallagher A (2024) Artificial intelligence and nursing: The good, the bad and the cautionary. Nurs Ethics 31(6):995\u0026ndash;998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/09697330241286344\u003c/span\u003e\u003cspan address=\"10.1177/09697330241286344\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorge A, Peirce AG (2025) Artificial Intelligence in Critical Care Nursing: Benefits, Risks, and Ethical Considerations. Crit Care Nurse 45(5):46\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4037/ccn2025746\u003c/span\u003e\u003cspan address=\"10.4037/ccn2025746\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHassanein S, El Arab RA, Abdrbo A, Abu-Mahfouz MS, Gaballah MKF, Seweid MM, Almari M, Alzghoul H (2025) Artificial intelligence in nursing: an integrative review of clinical and operational impacts. Front Digit Health 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fdgth.2025.1552372\u003c/span\u003e\u003cspan address=\"10.3389/fdgth.2025.1552372\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu M, Wang Y, Liu Y, Cai B, Kong F, Zheng Q, Zhao D, Gao G, Hui Z (2025) Artificial Intelligence in Nursing Decision-Making: A Bibliometric Analysis of Trends and Impacts. Nurs Rep 15(6):198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nursrep15060198\u003c/span\u003e\u003cspan address=\"10.3390/nursrep15060198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang K, Jiao Z, Cai Y, Zhong Z (2022) Artificial intelligence-based intelligent surveillance for reducing nurses\u0026rsquo; working hours in nurse\u0026ndash;patient interaction: A two‐wave study. J Nurs Adm Manag 30(8):3817\u0026ndash;3826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jonm.13787\u003c/span\u003e\u003cspan address=\"10.1111/jonm.13787\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwak Y, Ahn J-W, Seo YH (2022) Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students\u0026rsquo; behavioral intentions. BMC Nurs 21(1):267. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12912-022-01048-0\u003c/span\u003e\u003cspan address=\"10.1186/s12912-022-01048-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLattuca M, Maratta D, Beffert U, Chevrier A, Winer L (2023) Healthcare AI: A Revised Quebec Framework for Nursing Education. Qual Advancement Nurs Educ - Avanc\u0026eacute;es En Formation Infirmi\u0026egrave;re 9(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17483/2368-6669.1408\u003c/span\u003e\u003cspan address=\"10.17483/2368-6669.1408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohammad Amini M, Jesus M, Fanaei Sheikholeslami D, Alves P, Hassanzadeh Benam A, Hariri F (2023) Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate. Mach Learn Knowl Extr 5(3):1023\u0026ndash;1035. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/make5030053\u003c/span\u003e\u003cspan address=\"10.3390/make5030053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNashwan AJ, Abujaber A, Ahmed SK (2024) Charting the Future: The Role of AI in Transforming Nursing Documentation. \u003cem\u003eCureus\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.57304\u003c/span\u003e\u003cspan address=\"10.7759/cureus.57304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaladino MS (2023) An ethical pathway for the use of Artificial Intelligence in Medicine and Nursing Care. Med e Morale 72(2):121\u0026ndash;139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4081/mem.2023.1231\u003c/span\u003e\u003cspan address=\"10.4081/mem.2023.1231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRonquillo CE, Peltonen L, Pruinelli L, Chu CH, Bakken S, Beduschi A, Cato K, Hardiker N, Junger A, Michalowski M, Nyrup R, Rahimi S, Reed DN, Salakoski T, Salanter\u0026auml; S, Walton N, Weber P, Wiegand T, Topaz M (2021) Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs 77(9):3707\u0026ndash;3717. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jan.14855\u003c/span\u003e\u003cspan address=\"10.1111/jan.14855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSengul T, Sarik\u0026ouml;se S, Gul A (2025) Ethical decision-making and artificial intelligence in nursing education: An integrative review. Nurs Ethics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/09697330251366600\u003c/span\u003e\u003cspan address=\"10.1177/09697330251366600\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoriano GP, Yasuhara Y, Ito H, Matsumoto K, Osaka K, Kai Y, Locsin R, Schoenhofer S, Tanioka T (2022) Robots and Robotics in Nursing. \u003cem\u003eHealthcare\u003c/em\u003e 10(8), 1571. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/healthcare10081571\u003c/span\u003e\u003cspan address=\"10.3390/healthcare10081571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStokes F, Palmer A (2020) Artificial Intelligence and Robotics in Nursing: Ethics of Caring as a Guide to Dividing Tasks Between AI and Humans. Nurs Philos 21(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nup.12306\u003c/span\u003e\u003cspan address=\"10.1111/nup.12306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTabudlo J, Kuan L, Garma PF (2022) Can nurses in clinical practice ascribe responsibility to intelligent robots? Nurs Ethics 29(6):1457\u0026ndash;1465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/09697330221090591\u003c/span\u003e\u003cspan address=\"10.1177/09697330221090591\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerma R, Macaskill F, Kim A, Raison N, Dasgupta P (2025) Ethical aspects of artificial intelligence: what urologists need to know. Curr Opin Urol 35(3):224\u0026ndash;229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/MOU.0000000000001278\u003c/span\u003e\u003cspan address=\"10.1097/MOU.0000000000001278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVyas P, Gephart S (2025) Proposing Technology-Enhanced Wisdom. Adv Nurs Sci. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/ANS.0000000000000585\u003c/span\u003e\u003cspan address=\"10.1097/ANS.0000000000000585\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang S, Xia Z (2024) Development Trends of Artificial Intelligence Ethics. Open Access J Educ Lang Stud 2(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.19080/OAJELS.2024.02.555576\u003c/span\u003e\u003cspan address=\"10.19080/OAJELS.2024.02.555576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatson AL (2024) Ethical considerations for artificial intelligence use in nursing informatics. Nurs Ethics 31(6):1031\u0026ndash;1040. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/09697330241230515\u003c/span\u003e\u003cspan address=\"10.1177/09697330241230515\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWynn M (2025) The ethics of non-explainable artificial intelligence: an overview for clinical nurses. Br J Nurs 34(5):294\u0026ndash;297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12968/bjon.2024.0394\u003c/span\u003e\u003cspan address=\"10.12968/bjon.2024.0394\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYelne S, Chaudhary M, Dod K, Sayyad A, Sharma R (2023) Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare. \u003cem\u003eCureus\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.49252\u003c/span\u003e\u003cspan address=\"10.7759/cureus.49252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu J, Shi K, Yang C, Niu Y, Zeng Y, Zhang N, Liu T, Chu CH (2022) Ethical issues of smart home-based elderly care: A scoping review. J Nurs Adm Manag 30(8):3686\u0026ndash;3699. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jonm.13521\u003c/span\u003e\u003cspan address=\"10.1111/jonm.13521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Airlangga University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"artificial intelligence, nursing ethics, decision-making, bibliometric analysis, AI literacy","lastPublishedDoi":"10.21203/rs.3.rs-8033889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8033889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eThe integration of Artificial Intelligence (AI) into nursing practice is rapidly advancing, offering potential improvements in clinical accuracy, decision-making, and service efficiency. However, this acceleration also introduces ethical challenges related to data privacy, algorithmic transparency, accountability, and fairness in healthcare delivery.\u003c/p\u003e\u003ch2\u003eObjective:\u003c/h2\u003e\u003cp\u003eThis study aims to map the scientific landscape of ethical issues surrounding the use of AI in nursing practice through a bibliometric approach.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eData were retrieved from the Web of Science Core Collection on October 30, 2025, using keywords related to AI, ethics, and nursing. The inclusion criteria comprised English-language research articles and reviews published between 2019 and 2025. A total of 68 articles met the eligibility criteria and were analyzed using the Web of Science Analysis Tools, VOSviewer (v.1.6.20), and Microsoft Excel to map publication trends, author and institutional collaborations, and thematic structures based on co-authorship, bibliographic coupling, and keyword co-occurrence.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003ePublications increased sharply from one article in 2019 to 35 in 2025, indicating growing global attention to the ethical dimensions of AI in nursing. The United States, China, and the United Kingdom emerged as the leading contributors, with international researcher collaborations forming three distinct clusters. \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003eNursing Ethics\u003c/em\u003e, and \u003cem\u003eBMC Nursing\u003c/em\u003e were identified as the most influential journals. The dominant keywords included \u003cem\u003eartificial intelligence\u003c/em\u003e, \u003cem\u003enursing ethics\u003c/em\u003e, \u003cem\u003emachine learning\u003c/em\u003e, \u003cem\u003edecision-making\u003c/em\u003e, and \u003cem\u003eAI literacy\u003c/em\u003e, forming four thematic clusters: ethics and data, generative AI and decision-making, robotics in care, and digital literacy. The most cited articles highlighted the opportunities and challenges of generative AI in clinical practice and education.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eThe application of AI in nursing is rapidly expanding but is accompanied by significant ethical dilemmas. The bibliometric analysis revealed a global focus on privacy, accountability, algorithmic fairness, and nursing preparedness. Strengthening AI literacy, establishing ethical governance frameworks, and developing adaptive policies are essential to ensure the responsible and patient-centered implementation of AI technologies.\u003c/p\u003e","manuscriptTitle":"Exploring the Ethical Landscape of Artificial Intelligence in Nursing Practice: A Bibliometric Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 10:09:18","doi":"10.21203/rs.3.rs-8033889/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec20d89b-a51c-4e51-964b-454f81d412db","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57454743,"name":"Nursing"}],"tags":[],"updatedAt":"2025-11-06T20:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 10:09:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8033889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8033889","identity":"rs-8033889","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}