Trends in artificial intelligence in nursing: Impacts on nursing management.

To investigate the academic use of artificial intelligence (AI) in nursing. A bibliometric analysis combined with the VOSviewer software quantification method has been utilized for a literature analysis. In recent years, this approach has attracted the interest of scholars in various research fields. Thus far, there is no publication using bibliometric analysis combined with the VOSviewer software to analyse the applications of AI in nursing. A bibliometric analysis methodology was used to search for relevant articles published between 1984 and March 2022. Six databases, Embase, Scopus, PubMed, CINAHL, WoS and MEDLINE, were included to identify relevant studies, and data such as the year of publication, journals, country, institutional source, field and keywords were analysed. Most relevant articles were published from institutions in the United States. The League of European Research Universities has published most research studies that use AI and nursing. Scholars have mainly focused on nursing, medical informatics, computer science AI, healthcare sciences services and physics particles fields. Commonly used keywords were machine learning, care, AI, natural language processing, prediction and nurse. Research articles were mainly published in Nurse Education Today. Research topics such as AI-assisted medical recording and medical decision making were also identified. According to this study, AI in nursing has the potential to attract more attention from researchers and nursing managers. Additional high-quality research beyond the scope of medical education, as well as on cross-domain collaboration, is warranted to explore the acceptability and effective implementation of AI technologies. This study provides scholars and nursing managers with structured information regarding the use of AI in nursing based on scientific and technological developments across different fields and institutions. The application of AI can improve nursing management, nursing quality, safety management and team communication, as well as encourage future international collaboration.

Chang CY ，Jen HJ ，Su WS 《-》

Bibliometric analysis of core competencies associated nursing management publications.

This study aimed to identify high-impact papers on global nursing to determine and analyse the publication of articles on core competencies in nursing-related journals and the research trends in the era of globalization. Bibliometrics has been shown to be an effective method for analysing publications. Through bibliometrics, nursing managers and researchers can understand the trends of high-impact international nursing core competencies research, identify mainstream research directions and obtain relevant knowledge and information, thereby facilitating the translation of research outcomes into nursing management practice. The study adopted bibliometric analysis and the VOSviewer software to explore dynamic publication trends and analyse the current situation of nursing research from a comprehensive development perspective, which was realized by searching for nursing core competencies papers in the Web of Science (WoS) database, calculating citations and determining the trends of the most influential papers. Nursing core competencies research grew rapidly between 1997 and 2022. Countries with the most core competencies publications were the United States, England, Australia and Canada. The Journal of Nursing Management has attracted substantial attention from researchers worldwide. Education, Management and Nurses were the most frequently used keywords in the study. A total of 534 papers were retrieved from the WoS database with the main research fields, including nursing, business economics, public environmental occupational health and health care science services. Equipping nursing graduates with core competencies has always been an important goal of global medical and nursing education. This study analysed papers across 35 years, most of which were published in the Journal of Nursing Management. In addition, the study identified some of the main research topics of nursing management, such as the integration of education with nursing management and the cultivation of nurses' core competencies. The study also provides a fresh review of highly cited articles. The results of the study show that high-quality articles play the role of improving both the quality and the quantity of related research. By analysing the trends of the research on core competencies, this study lays a bibliometric foundation for researchers regarding international journals, hot topics and relevant fields. In addition, the highly cited articles reveal new perspectives for the nursing field, providing inspiration for nursing management and education researchers. This study provides scholars and managers with an overview of the current situation of nursing management research and the development of benchmark journals. The study provides researchers not only with a better understanding of various international journals, allowing them to transition out of traditional thinking in the era of science and technology, but also with innovative thinking by combining research with nursing management. The results of this study invite nursing managers to study relevant topics of core competencies and integrate information technology to education, management and nurses, thereby contributing to nursing management and educational research.

Su WS ，Hwang GJ ，Chang CY 《-》

Global output on artificial intelligence in the field of nursing: A bibliometric analysis and science mapping.

To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty-one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN-Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting-edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Although AI has been paid more and more attention to the field of nursing, there is still a lack of high-yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi-disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions.

Shi J ，Wei S ，Gao Y ，Mei F ，Tian J ，Zhao Y ，Li Z ... -  《-》

Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study.

The past decade has seen major advances in the use of artificial intelligence (AI) to solve various biomedical problems, including cancer. This has resulted in more than 6000 scientific papers focusing on AI in oncology alone. The expansiveness of this research area presents a challenge to those seeking to understand how it has developed. A scientific analysis of AI in the oncology literature is therefore crucial for understanding its overall structure and development. This may be addressed through bibliometric analysis, which employs computational and visual tools to identify research activity, relationships, and expertise within large collections of bibliographic data. There is already a large volume of research data regarding the development of AI applications in cancer research. However, there is no published bibliometric analysis of this topic that offers comprehensive insights into publication growth, co-citation networks, research collaboration, and keyword co-occurrence analysis for technological trends involving AI across the entire spectrum of oncology research. The purpose of this study is to investigate documents published during the last decade using bibliometric indicators and network visualization. This will provide a detailed assessment of global research activities, key themes, and AI trends over the entire breadth of the oncology field. It will also specifically highlight top-performing authors, organizations, and nations that have made major contributions to this research domain, as well as their interactions via network collaboration maps and betweenness centrality metric. This study represents the first global investigation of AI covering the entire cancer field and using several validated bibliometric techniques. It should provide valuable reference material for reorienting this field and for identifying research trajectories, topics, major publications, and influential entities including scholars, institutions, and countries. It will also identify international collaborations at three levels: micro (that of an individual researcher), meso (that of an institution), and macro (that of a country), in order to inform future lines of research. The Science Citation Index Expanded from the Web of Science Core Collection was searched for articles and reviews pertaining exclusively to AI in cancer from 2012 through 2022. Annual publication trends were plotted using Microsoft Excel 2019. CiteSpace and VOSViewer were used to investigate the most productive countries, researchers, journals, as well as the sharing of resources, intellectual property, and knowledge base in this field, along with the co-citation analysis of references and keywords. A total of 6757 documents were retrieved. China produced the most publications of any country (2087, 30.89%), and Sun Yat Sen University the highest number (167, 2.47%) of any institute. WEI WANG was the most prolific author (33, 0.49%). RUI ZHANG ranked first for highest betweenness centrality (0.21) and collaboration criteria. Scientific Reports was found to be the most prolific journal (208, 3.18%), while PloS one had the most co-citations (2121, 1.55%). Strong and ongoing citation bursts were found for keywords such as "tissue microarray", "tissue segmentation", and "artificial neural network". Deep learning currently represents one of the most cutting-edge and applicable branches of AI in oncology. The literature to date has dealt extensively with radiomics, genomics, pathology, risk stratification, lesion detection, and therapy response. Current hot topics identified by our analysis highlight the potential application of AI in radiomics and precision oncology.

Wu T ，Duan Y ，Zhang T ，Tian W ，Liu H ，Deng Y ... -  《-》

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

To explore research hotspots and theme trends in artificial intelligence in nurse education using bibliometric analysis. Bibliometric analysis. Literature from the Web of Science Core Collection from the time of construction to October 31, 2023 was searched. Analyses of countries, authors, institutions, journals, and keywords were conducted using Bibliometrix (based on R language), CiteSpace, the online analysis platform (bibliometric), Vosviewer, and Pajek. A total of 135 articles with a straight upward trend over the last three years were retrieved. By fitting the curve R2 = 0.6022 (R2 > 0.4), we predicted that the number of annual articles is projected to grow in the coming years. The United States (n = 38), the National University of Singapore (n = 16), Professor Jun Ota (n = 8), and Nurse Education Today (n = 14) are the countries, institutions, authors, and journals that contributed to the most publications, respectively. Collaborative network analysis revealed that 32 institutional and 64 author collaborative teams were established. We identified ten high-frequency keywords and nine clusters. We categorized the research hotspots of artificial intelligence in nurse education into three areas: (1) Artificial intelligence-enhanced simulation robots, (2) machine learning and data mining, and (3) large language models based on natural language processing and deep learning. By analyzing the temporal and spatial evolution of keywords and burst detection, we found that future research trends may include (1) expanding and deepening the application of AI technology, (2) assessment of behavioral intent and educational outcomes, and (3) moral and ethical considerations. Future research should be conducted on technology applications, behavioral intent, ethical policy, international cooperation, interdisciplinary cooperation, and sustainability to promote the continued development and innovation of AI in nurse education.

Luo C ，Mao B ，Wu Y ，He Y ... -  《-》