Global Trends on the Use of Artificial Intelligence in Nursing: A Descriptive and Evaluative Bibliometric Analysis Study

Yıl 2024, Cilt: 6 Sayı: 2, 149 - 156, 03.08.2024

Fatma Azizoğlu , Banu Terzi

https://doi.org/10.48071/sbuhemsirelik.1467622

Öz

Introduction: There is a need to examine the use of artificial intelligence in the branch of nursing and to investigate the
characteristics of the research conducted in this field.
Aim: The aimed was to examine the characteristics of the current knowledge structure and development process in the
field of the use of artificial intelligence in nursing.
Method: In the descriptive and evaluative bibliometric analysis study, data were obtained from Web of Science database.
All relevant studies conducted between 2004 and 2023 were included in the study. Data analysis was performed using
R Biblioshniy software. Two hundred seventy-three studies were included in the study.
Results: The most publications (n = 86, 31.50%) were made in this field in 2022. The most productive author in the field of nursing and artificial intelligence was Topaz, Maxim. The prominent topics in the studies were "virtual reality, artificial intelligence, nursing, machine learning, simulation, nursing education, education, pain, nursing students, natural language processing, nurses, robotics, deep learning and mental health".
Conclusion: There has been a significant increase in the number of studies on the use of artificial intelligence in nursing
and this area offers an active field of study for nursing researchers

Anahtar Kelimeler

Artificial intelligence, bibliometric analysis, deep learning, nursing, virtual reality

Hemşirelikte Yapay Zekanın Kullanımına İlişkin Küresel Eğilimler: Tanımlayıcı ve Değerlendirici Bir Bibliyometrik Analiz Çalışması

Öz

Giriş: Yapay zekânın hemşirelik alanında kullanımı hakkında fikir sahibi olabilmek ve bu alandaki uygulama ve araştırmalardaki gelişmeleri tespit edebilmek için ilgili yayınların özelliklerinin araştırılması gerekmektedir.
Amaç: Hemşirelikte yapay zekâ kullanımı alanında mevcut bilgi yapısı ve gelişim sürecinin özelliklerinin incelenmesi
amaçlandı.
Yöntem: Tanımlayıcı ve değerlendirici bibliyometrik analiz çalışmasında veriler “Web of Science” veri tabanından elde
edilmiştir. Çalışmaya 2004-2023 yılları arasında yapılmış ilgili tüm çalışmalar dahil edilmiştir. Veri analizi R Biblioshniy
yazılımı kullanılarak gerçekleştirilmiştir. Çalışmaya iki yüz yetmiş üç çalışma dahil edilmiştir.
Bulgular: Bu alanda en fazla yayın (n = 86, %31,50) 2022 yılında yapılmıştır. Hemşirelik ve yapay zekâ alanında en
üretken yazar Topaz, Maxim’dir. Çalışmalarda "sanal gerçeklik, yapay zekâ, hemşirelik, makine öğrenmesi, simülasyon,
hemşirelik eğitimi, eğitim, ağrı, hemşirelik öğrencileri, doğal dil işleme, hemşireler, robotik, derin öğrenme ve ruh
sağlığı" konuları öne çıkmıştır.
Sonuç: Hemşirelikte yapay zekâ kullanımına ilişkin çalışmaların sayısında önemli bir artış olmuştur ve bu alan hemşirelik
araştırmacıları için aktif bir çalışma alanı sunmaktadır.

Anahtar Kelimeler

Bibliyometrik analiz, derin öğrenme, hemşirelik, sanal gerçeklik, yapay zeka

Kaynakça

• Abbasgholizadeh Rahimi, S., Légaré, F., Sharma, G., Archambault, P., Vignon Zomahoun, H. T., Chandavong, S., …, & Légaré, J. (2021). Application of artificial intelligence in community-based primary health care: Systematic scoping review and critical appraisal. Journal of Medical Internet Research, 23(9), e29839. doi: 10.2196/29839
• Ahuja, A. S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. PeerJ, 47, e7702. doi: 10.7717/peerj.7702
• Akgerman, A., Özdemir, Yavuz, E. D., Kavaslar, I., & Güngör, S. (2022). Artificial intelligence and nursing. Journal of Artifical Intelligence in Health Sciences, 2(1), 21-27. doi: 0.52309/jaihs.v2i1.36
• Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. doi: 10.1016/j.joi.2017.08.007
• Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179-255. doi: 10.1002/aris.1440370106
• Butt, A. L., Kardong-Edgren, S., & Ellertson, A. (2018). Using game-based virtual reality with haptics for skill acquisition. Clinical Simulation in Nursing, 16, 25-32. doi: 10.1016/j.ecns.2017.09.010
• Carter-Templeton, H., Frazier, R. M., & Wu, L. T. H. W. (2018). Robotics in nursing: A bibliometric analysis. Journal of Nursing Scholarship, 50(6), 582-589. doi: 10.1111/jnu.12399
• Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377. doi: 10.1002/asi.20317
• Clancy, T. R. (2020). Artificial intelligence and nursing: the future is now. Journal of Nursing Administration, 50(3), 125-127. doi: 10.1097/ NNA.0000000000000855
• Ertekin, C. (2014). Scientific research and scientific performance evaluation. Turkish Journal of Neurology, 20(2), 32-36. doi:10.4274/tnd.80269
• Esen, M., Bellibas, M. S., & Gumus, S. (2020). The evolution of leadership research in higher education for two decades (1995-2014): A bibliometric and content analysis. International Journal of Leadership in Education, 23(3), 259-273. doi: 10.1080/13603124.2018.1508753
• Garfield, E. (2006). The history and meaning of the journal impact factor. Journal of the American Medical Association, 295(1), 90-93. doi: 10.1001/ jama.295.1.90
• Guleria, D., & Kaur, G. (2021). Bibliometric analysis of ecopreneurship using VOSviewer and R Studio Bibliometrix, 1989–2019. Library Hi Tech, 1(24), 1001-1024. doi: 1108/LHT-09-2020-0218
• 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. doi: 10.2196/18228
• Harmon, J., Pitt, V., Summons, P., & Inder, K. J. (2021). Use of artificial intelligence and virtual reality within clinical simulation for nursing pain education: A scoping review. Nurse Education Today, 97, 104700. doi: 10.1016/j.nedt.2020.104700
• Jan van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. doi: 10.1007/s11192-009-0146-3
• Karagöz, B., & Şeref, İ. (2019). Bibliometric profile of Journal of Values Education (2009-2018). Journal of Values Education, 17(37), 219-246. doi: 10.34234/ded.507761
• Liu, S., Zhang, R. Y., & Kishimoto, T. (2021). Analysis and prospect of clinical psychology based on topic models: Hot research topics and scientific trends in the latest decades. Psychology, Health and Medicine, 6(4), 395-407. doi: 10.1080/13548506.2020.1738019
• López-Robles, J. R., Cobo, M. J., Gutiérrez-Salcedo, M., María Ángeles, M. S., Enrique, H. V., & Nadia Karina, G. R. (2021). 30th anniversary of applied intelligence: A combination of bibliometrics and thematic analysis using SciMAT. Applied Intelligence, 51(9), 6547-6568. doi: 10.1007/ s10489-021-02584-z
• McGrow, K. (2019). Artificial intelligence. Essentials for nursing. Nursing, 46(9), 46-49. doi: 10.1097/01.NURSE.0000577716.57052.8d Mingers, J., & Leydesdorff, L. (2015). A review of theory and practice in scientometrics. European Journal of Operational Research, 246(1),1-19. doi: 10.1016/j.ejor.2015.04.002
• Moral-Munoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Sofware tools for conducting bibliyometric analysis in sience: An -up-to-date rewiew. El Profesional De La Informacion, 29(1), 273-289. doi: 10.3145/epi.2020.ene.03
• O’Connor, S. (2023). Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education Practise, 66, 103537. doi: 10.1016/j.nepr.2023.103572
• Ramos-Rincón, J. M., Pinargote-Celorio, H., Belinchón-Romero, I., & González-Alcaide, G. (2019). A snapshot of pneumonia research activity and collaboration patterns (2001-2015): A global bibliometric analysis. BMC Medical Research Methodology, 19(184), 1-17. doi: 10.1186/ S12874-019-0819-4
• Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30-39. doi: 10.1097/01.NUMA.0000578988.56622.21
• Ronquillo, C. E., Peltonen, L. M., Pruinelli, L., Chu, C. H., Bakken, S., Beduschiet, A., … & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invita-tional think-tank of the nursing and artificial intelligence leadership collaborative. Journal of Advanced Nursing, 77(9), 3707-3717. doi: 10.1111/jan.14855
• Schneider, S. M., Prince-Paul, M., Allen, M. J., Silverman, P., & Talaba, D. (2004). Virtual reality as a distraction intervention for women receiving chemotherapy. Oncology Nursing Forum, 31(1), 81-88. doi: 10.1188/04. ONF.81-88
• Shi, J., Gao, Y., Ming, L., Yang, K., Sun, Y., Chen, J., ..., & Tian, J. (2021). A bibliometric analysis of global research output on network meta-analysis. BMC Medical Informatics and Decision Making, 21(1),144. doi: 10.1186/ s12911-021-01470-5
• Shi, J., Wei, S., Gao, Y., Mei, F., Tian, J., Zhao, Y., …, & Li, Z. (2023). Global output on artificial intelligence in the field of nursing: A bibliometric analysis and science mapping. Journal of Nursing Scholarship, 55, 853-863. doi:10.1111/jnu.12852
• Sott, M. K., Bender, M. S., Furstenau, L. B., Machado, L. M., Cobo, M. J., & Bragazzi, N. L. (2020). 100 years of scientific evolution of work and organizational psychology: A bibliometric network analysis from 1919 to 2019. Frontiers in Psychology, 11, 1-15. doi: 10.3389/fpsyg.2020.598676
• Von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., …, & Peltonen, L. M. (2022). Artificial intelligence -based technologies in nursing: A scoping literature re-view of the evidence. International Journal of Nursing Studies, 127, 104153. doi: 10.1016/j.ijnurstu.2021.104153
• Waltman, L., & Jan van Eck, N. (2013). A smart local moving algorithm for large-scale modularity-based community detection. The European Physical Journal B, 86(11), 1-14. doi: 10.1140/epjb/e2013-40829-0
• Xu, J., Li, M., Gao, Y., Liu, M., Shi, S., Shi, J., …, & Tian, J. (2022). Using mendelian randomization as the cornerstone for causal inference in epidemiology. Environmental Science and Pollution Research International, 29(4), 5827-5839. doi: 10.1007/s11356-021-15939-3.
• Yanbing, S., Ruifang, Z., Chen, W., Shifan, H., Hua, L., & Zhiguang, D. (2020). Bibliometric analysis of Journal of Nursing Management from 1993 to 2018. Journal of Nursing Management, 28(2), 317-331. doi: 10.1111/jonm.12925
• Zavadskas, E. K., Skibniewski, M. J., & Antucheviciene, J. (2014). Performance analysis of civil Engineering Journals based on the web of science® database. Archives of Civil and Mechanical Engineering, 14(4), 519-527. doi: 10.1016/j.acme.2014. 05.008
• Zhu, J., Song, L. J., Zhu, L., & Johnson, R. E. (2019). Visualizing the landscape and evolution of leadership research. The Leadership Quarterly, 30(2), 215-232. doi: 10.1016/J.LEAQUA. 2018.06.003