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Trends and Current Topics in Artificial Intelligence in Nursing Research: A Bibliometric Analysis and Science Mapping

Year 2024, Volume: 13 Issue: 2, 324 - 338, 28.08.2024
https://doi.org/10.53424/balikesirsbd.1406477

Abstract

Objective: As AI's role in nursing grows, it is vital to understand its impact and challenges. Using bibliometric analysis, this study aimed to identify and examine the prevailing trends and current topics in artificial intelligence research within nursing. Materials and Methods: This was a retrospective bibliometric study. Study data were collected from WoSCC on August 08, 2023. Analyses were made through science mapping, Microsoft Excel, and VOSviewer. Results: The study included 316 publications dated 1984-2023. There was a rapid increase in publications and citations from 2018-2023. Related publications were made by 1148 authors. The journal "CIN-Computers, Informatics, Nursing" emerged as the most frequently published and cited journal. Fifty-three countries contributed to the publications, of which 45.2% were produced in the USA. The current topics were patient safety, depression, ChatGPT, and Chatbot in recent years. Conclusion: This bibliometric study shows a synergy between the general policies of countries on artificial Intelligence in recent years and the increasing number of publications in the last four years. However, this study also reveals that research on artificial intelligence in nursing is a nascent field. Managers and research nurses should lead the use of AI applications in nursing services management and nursing training and should encourage research on the topic.
Key Words: Artificial Intelligence, Nursing, Bibliometric Analysis, Research Trends, VOSviewer

Ethical Statement

Given the nature of this bibliometric study, no ethics committee approval was required.

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References

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Hemşirelikte Yapay Zekâ Araştırmalarında Trendler ve Güncel Konular: Bibliyometrik Analiz ve Bilimsel Haritalama

Year 2024, Volume: 13 Issue: 2, 324 - 338, 28.08.2024
https://doi.org/10.53424/balikesirsbd.1406477

Abstract

Amaç: Bu çalışmada, bibliyometrik analiz kullanarak hemşirelik alanındaki yapay zekâ araştırmalarındaki mevcut eğilimleri ve güncel konuları belirlemeyi ve incelemeyi amaçlanmıştır. Gereç ve Yöntem: Bu retrospektif bibliyometrik bir çalışmadır. Araştırmanın verileri 08 Ağustos 2023 tarihinde Web of Science Core Collection (WoSCC) veri tabanından toplanmıştır. Verilerin analizinde bilimsel haritalama analizi yapılmış ve Microsoft Excel ve VOSviewer programları kullanılmıştır. Bulgular: Çalışma, 1984-2023 tarihleri arasında 316 yayını kapsamaktadır. 2018-2023 yılları arasında yayın ve atıflarda hızlı bir artış görülmüştür. İlgili yayınlar 1148 yazar tarafından yapılmıştır. "CIN-Computers, Informatics, Nursing" dergisi, en çok yayınlanan ve alıntılanan dergi olarak öne çıkmıştır. Elli üç ülke bu yayınlara katkıda bulunmuş, bunların %45.2'si ABD'de üretilmiştir. Son yıllarda güncel konular son yıllarda hasta güvenliği, depresyon, ChatGPT ve Chatbot olmuştur. Sonuç: Bu bibliyometrik çalışma, son yıllarda ülkelerin yapay zekâ konusunda belirledikleri genel politikalar ile son dört yılda artan yayın sayısı arasında bir sinerji oluştuğunu göstermektedir. Bununla birlikte, yapay zekâ çalışmalarının hemşirelik alanında yeni ve henüz kuluçka döneminde bir alan olduğunu ortaya koymaktadır. Yönetici ve araştırmacı hemşireler yapay zekâ uygulamalarının hemşirelik hizmetleri yönetiminde ve hemşirelik eğitiminde kullanımına yönelik önderlik etmeli ve konuyla ilgili araştırmaların yapılmasını teşvik etmelidir
Anahtar Kelimeler: Yapay Zekâ, Hemşirelik, Bibliyometrik Analiz, Araştırma Trendleri, VOSviewer

Project Number

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References

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  • Alaiad, A., & Zhou, L. (2014). The determinants of home healthcare robots adoption: An empirical investigation. International journal of medical informatics, 83(11), 825-840. https://doi.org/10.1016/j.ijmedinf.2014.07.003
  • Alderden, J., Pepper, G. A., Wilson, A., Whitney, J. D., Richardson, S., Butcher, R., Jo Y., & Cummins, M. R. (2018). Predicting pressure injury in critical care patients: a machine-learning model. American Journal of Critical Care, 27(6), 461–468. https://doi.org/10.4037/ajcc2018525
  • Archibald, M. M., & Barnard, A. (2018). Futurism in nursing: Technology, robotics and the fundamentals of care. Journal of Clinical Nursing, 27(11-12), 2473–2480. https://doi.org/10.1111/jocn.14081
  • Aristoteles, A., Abie, P. K., Irawati, A. R., Sakethi, D., Lisa, S., Dedy Miswar, D. M., & Rika, N. A. (2023). Development of Nursing Process Expert System for Android-based Nursing Student Learning. International Journal of Advanced Computer Science and Applications, 14(11), 234-239. https://doi.org/10.14569/IJACSA.2023.0141122
  • Backonja, U., Hall, A. K., Painter, I., Kneale, L., Lazar, A., Cakmak, M., Thompson, H. H., & Demiris, G. (2018). Comfort and attitudes towards robots among young, middle‐aged, and older adults: a cross‐sectional study. Journal of Nursing Scholarship, 50(6), 623–633. https://doi.org/10.1111/jnu.12430
  • Bahroun, Z., Anane, C., Ahmed, V., Zacca, A. (2023). Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis. Sustainability. 15(17), 12983. https://doi.org/10.3390/su151712983
  • Bates, D. W., Auerbach, A., Schulam, P., Wright, A., & Saria, S. (2020). Reporting and implementing interventions involving machine learning and artificial intelligence. Annals of Internal Medicine, 172(11_Supplement), S137-S144. https://doi.org/10.7326/M19-0872
  • Berger, A. M., & Berger, C. R. (2004). Data mining as a tool for research and knowledge development in nursing. CIN: Computers, Informatics, Nursing, 22(3), 123-131.
  • Booth, BE. (2011). Robotics in nursing. Journal of Practical Nursing, 61(4), 12-13. Retrieved from https://www.proquest.com/openview/7449a9306fb3f6a0f42b039b653fabb2/1
  • Bose, E., Maganti, S., Bowles, K. H., Brueshoff, B. L., & Monsen, K. A. (2019). Machine learning methods for identifying critical data elements in nursing documentation. Nursing Research, 68(1), 65–72. https://doi.org/10.1097/NNR.0000000000000315.
  • Carroll, W. M. (2018). Artificial intelligence, nurses, and the quadruple aim. Online Journal of Nursing Informatics, 22(2), 3–1.
  • Chang, C. Y., Gau, M. L., Tang, K. Y., & Hwang, G. J. (2021). Directions of the 100 most cited nursing student education research: A bibliometric and co-citation network analysis. Nurse Education Today, 96, 104645. https://doi.org/10.1016/j.nedt.2020.104645
  • Chang, C. Y., Jen, H. J., & Su, W. S. (2022). Trends in artificial intelligence in nursing: Impacts on nursing management. Journal of Nursing Management, 30(8), 3644-3653. https://doi.org/10.1111/jonm.13770
  • Choi, E.P.H., Lee, J.J., Ho, M.H., Kwok, J.Y.Y., & Lok, K.Y.LW. (2023). Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse education. Nurse Education Today, 125, 105796-105796. https://doi.org/10.1016/j.nedt.2023.105796
  • Conn A. (2018). Artificial Intelligence Policy-Japan. Retrieved from https://futureoflife.org/2018/07/12/ai-policy-japan/
  • Courtney, K. L., Alexander, G. L., & Demiris, G. (2008). Information technology from novice to expert: implementation implications. Journal of Nursing Management, 16(6), 692–699. https://doi.org/10.1111/j.1365-2834.2007.00829.x
  • Çiçek Korkmaz, A., & Altuntaş, S. (2022). A bibliometric analysis of COVID‐19 publications in nursing by visual mapping method. Journal of Nursing Management, 30(6), 1892-1902. https://doi.org/10.1111/jonm.13636
  • Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
  • Downing, C., Temane, A., Bader, S. G., Hillyer, J. L., Beatty, S. C., & Hastings-Tolsma, M. (2021). International nursing research collaboration: Visualizing the output and impact of a Fulbright Award. International Journal of Africa Nursing Sciences, p. 15, 100380. https://doi.org/10.1016/j.ijans.2021.100380
  • Easton-Garrett, S., Gephart, S., & Nickels, S. (2020). Utilizing artificial intelligence for falls management in memory care. Geriatric Nursing (New York, NY). 10.1016/j.gerinurse.2020.03.011
  • Ermağan, İ. (2021). Worldwide Artificial Intelligence Studies with a Comparative Perspective: How Ready is Turkey for This Revolution? In Artificial Intelligence Systems and the Internet of Things in the Digital Era. EAMMIS 2021, 1st ed.; Lecture Notes in Networks and, Systems; Musleh, A.M., Razzaque, A., Kamal, M.M., Eds.; Springer: Cham, Switzerland, 239, pp. 500–512.
  • Galetsi, P., & Katsaliaki, K. (2020). Big data analytics in health: An overview and bibliometric study of research activity. Health Information & Libraries Journal, 37(1), 5-25. https://doi.org/10.1111/hir.12286
  • Gunawan, J. (2023). Exploring the future of nursing: Insights from the ChatGPT model. Belitung Nursing Journal, 9(1), 1-5. https://doi.org/10.33546/bnj.2551
  • Guo, Y., Hao, Z., Zhao, S., Gong, J., & Yang, F. (2020). Artificial intelligence in health care: bibliometric analysis. Journal of Medical Internet Research, 22(7), e18228. https://doi.org/10.2196/18228
  • Gustafsson, C., Svanberg, C., & Müllersdorf, M. (2015). Using a robotic cat in dementia care: a pilot study. Journal of gerontological nursing, 41(10), 46-56. https://doi.org/10.3928/00989134-20150806-44
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Details

Primary Language English
Subjects Nurse Education, Nursing Workforce, Nursing Management, Nursing (Other)
Journal Section Articles
Authors

Ayşe Çiçek Korkmaz 0000-0001-8184-1490

Project Number -
Publication Date August 28, 2024
Submission Date December 19, 2023
Acceptance Date March 6, 2024
Published in Issue Year 2024 Volume: 13 Issue: 2

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APA Çiçek Korkmaz, A. (2024). Trends and Current Topics in Artificial Intelligence in Nursing Research: A Bibliometric Analysis and Science Mapping. Balıkesir Sağlık Bilimleri Dergisi, 13(2), 324-338. https://doi.org/10.53424/balikesirsbd.1406477

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