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

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

Keywords

• Yapay Zekâ
• Hemşirelik
• Bibliyometrik Analiz
• Araştırma Trendleri
• VOSviewer

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

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

Keywords

• Artificial Intelligence
• Nursing
• Bibliometric Analysis
• Research Trends
• VOSviewer

Ethical Statement

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

References

1. Ahmed, S.K. (2023). The Impact of ChatGPT on the Nursing Profession: Revolutionizing Patient Care and Education. Ann Biomed Eng 51, 2351–2352. https://doi.org/10.1007/s10439-023-03262-6
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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

9. 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.
10. Booth, BE. (2011). Robotics in nursing. Journal of Practical Nursing, 61(4), 12-13. Retrieved from https://www.proquest.com/openview/7449a9306fb3f6a0f42b039b653fabb2/1
11. 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.
12. Carroll, W. M. (2018). Artificial intelligence, nurses, and the quadruple aim. Online Journal of Nursing Informatics, 22(2), 3–1.
13. 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
14. 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
15. 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
16. Conn A. (2018). Artificial Intelligence Policy-Japan. Retrieved from https://futureoflife.org/2018/07/12/ai-policy-japan/
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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.
23. 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
24. 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
25. 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
26. 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
27. Gümüş, E., & Kasap, E. U. (2021). The Future of the Nursing: Robot Nurses. Journal of Artificial Intelligence in Health Sciences,1(2), 20-25. https://doi.org/10.52309/jai.2021.10
28. Ho, Y. S., & Wang, M. H. (2020). A bibliometric analysis of artificial intelligence publications from 1991 to 2018. COLLNET Journal of Scientometrics and Information Management, 14(2), 369-392. https://doi.org/10.1080/09737766.2021.1918032
29. Hyun, S., Johnson, S. B., & Bakken, S. (2009). Exploring the ability of natural language processing to extract data from nursing narratives. Computers, informatics, nursing: CIN, 27(4), 215. 10.1097/NCN.0b013e3181a91b58 Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https://doi.org/10.1007/s12525-021-00475-2
30. Javaid, M., Haleem, A., Singh, R.P., Khan, S., & Khan, I.H. (2023). Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. Bench Council Transactions on Benchmarks, Standards and Evaluations, 3(2), 100115. https://doi.org/10.1016/j.tbench.2023.100115
31. Jeong, G. H. (2020). Artificial intelligence, machine learning, and deep learning in women’s health nursing. Korean Journal of Women Health Nursing, 26(1), 5–9. https://doi.org/10.4069/kjwhn.2020.03.11
32. Kantek, F., & Yesilbas, H. (2020). Conflict in nursing studies: A bibliometric analysis of the top 100 cited papers. Journal of Advanced Nursing, 76(10), 2531– 2546. https://doi.org/10.1111/jan.14463
33. Kokol, P., Završnik, J., & Vošner, H. B. (2018). Bibliographic-based identification of hot future research topics: an opportunity for hospital librarianship. Journal of Hospital Librarianship, 18(4), 315-322. https://doi.org/10.1080/15323269.2018.1509193
34. Konttila, J., Siira, H., Kyngäs, H., Lahtinen, M., Elo, S., Kääriäinen, M., ... & Mikkonen, K. (2019). Healthcare professionals’ competence in digitalisation: A systematic review. Journal of Clinical Nursing, 28(5-6), 745-761. https://doi.org/10.1111/jocn.14710
35. Korkmaz, A., Aktürk, C., & Talan, T. (2023). Analyzing the users' sentiments of ChatGPT using Twitter data. Iraqi Journal for Computer Science and Mathematics, 4(2), 202-214. https://doi.org/10.52866/ijcsm.2023.02.02.018
36. Kwon, J. Y., Karim, M. E., Topaz, M., & Currie, L. M. (2019). Nurses “seeing forest for the trees” in the age of machine learning: using nursing knowledge to improve relevance and performance. CIN: Computers, Informatics, Nursing, 37(4), 203–212. http://dx.doi.org/10.1097/CIN.0000000000000508
37. Lee, J. Y., Song, Y. A., Jung, J. Y., Kim, H. J., Kim, B. R., Do, H. K., & Lim, J. Y. (2018). Nurses’ needs for care robots in integrated nursing care services. Journal of Advanced Nursing, 74(9), 2094-2105. https://doi.org/10.1111/jan.13711
38. Liu, J., Liu, F., Fang, J., & Liu, S. (2023). The application of Chat Generative Pre-trained Transformer in nursing education. Nursing Outlook, 71(6), 102064. https://doi.org/10.1016/j.outlook.2023.102064
39. 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, 26 (4),395– 407. https://doi.org/10.1080/13548506.2020.1738019
40. Maalouf, N., Sidaoui, A., Elhajj, I. H., & Asmar, D. (2018). Robotics in nursing: a scoping review. Journal of Nursing Scholarship, 50(6), 590-600. https://doi.org/10.1111/jnu.12424
41. New Generation of Artificial Intelligence Development Plan. The Foundation for Law and International Affairs. State Council Document, July 08, 2017, No. 35. Retrieved from https://flia.org/notice-state-council-issuing-new-generation-artificial-intelligence-development-plan/.
42. O'Connor, S. (2022). Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice, 66, 103537–103537. https://doi.org/10.1016/j.nepr.2022.103537
43. Oermann, M. H., Chinn, P. L., Carter-Templeton, H., & Nicoll, L. H. (2019). The importance of nursing in nursing publications. Nurse Author & Editor, 29(3), 1–6. https://doi.org/10.1111/j.1750-4910.2019.tb00043.x
44. Özturk, O. (2021). A framework for the design of bibliometric research. Oğuzhan Öztürk and Gökhan Gürler (Ed.). In Bibliometric Analysis as a Literature Review Tool (2nd ed, pp. 33–48), Ankara: Nobel publishing house
45. Palaganas, J. C., Fey, M., & Simon, R. (2016). Structured debriefing in simulation-based education. AACN Advanced Critical Care, 27(1), 78-85. https://doi.org/10.4037/aacnacc2016328
46. Papadopoulos, I., & Koulouglioti, C. (2018). The influence of culture on attitudes towards humanoid and animal‐like robots: An Integrative Review. Journal of Nursing Scholarship, 50(6), 653–665. https://doi.org/10.1111/jnu.12422
47. Papadopoulos, I.,Wright, S., Koulouglioti, C., Ali, S., Lazzarino, R.,Martín-García, ́A., … & Nissim, S. (2023). Socially assistive robots in health and social care: Acceptance and cultural factors. Results from an exploratory international online survey.Japan Journal of Nursing Science,20(2), e12523. https://doi.org/10.1111/jjns.12523
48. Pepito, J. A., & Locsin, R. (2019). Can nurses remain relevant in a technologically advanced future? International journal of nursing sciences, 6(1), 106-110. https://doi.org/10.1016/j.ijnss.2018.09.013
49. Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30–39. https://doi.org/10.1097/01.NUMA.0000578988.56622.21
50. Sharma, M., & Sharma, S. (2023). A holistic approach to remote patient monitoring, fueled by ChatGPT and Metaverse technology: The future of nursing education. Nurse Education Today, 131, 105972. https://doi.org/10.1016/j.nedt.2023.105972
51. Shi, J., Wei, S., Gao, Y., Mei, F., Tian, J., Zhao, Y., & Li, Z. (2022). Global output on artificial intelligence in nursing: A bibliometric analysis and science mapping. Journal of Nursing Scholarship, 55(4), 853–863. https://doi.org/10.1111/jnu.12852
52. Stokes, F., & Palmer, A. (2020). Artificial intelligence and robotics in nursing: ethics of caring as a guide to dividing tasks between AI and humans. Nursing Philosophy, 21(4), e12306. https://doi.org/10.1111/nup.12306
53. Sullivan, S. S., Hewner, S., Chandola, V., & Westra, B. L. (2019). Mortality risk in homebound older adults predicted from routinely collected nursing data. Nursing Research, 68(2), 156–166. https://doi.org/10.1097/NNR.000000000000032
54. Sweileh, W. M., Huijer, H. A., Al‐Jabi, S. W., Zyoud, S. H., & Sawalha, A. F. (2019). Nursing and midwifery research activity in Arab countries from 1950 to 2017. BMC Health Services Research, 19, 340. https://doi.org/10.1186/s12913-019-4178-y
55. Şendir, M., Şimşekoğlu, N., Kaya, A., Sümer, K. (2019). Nursing in Future Technology. University of Health Sciences Journal of Nursing, 1(3), 209-214.
56. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
57. Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q. H., Ho, M. T., Vuong, T. T., ... & Ho, R. (2019). Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. Journal of Clinical Medicine, 8(3), 360. https://doi.org/10.3390/jcm8030360
58. Von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., ... & Peltonen, L. M. (2021). Artificial Intelligence-based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies, 104153. https://doi.org/10.1016/j.ijnurstu.2021.104153
59. Yan, S., Zhang, H., & Wang, J. (2022). Trends and hot topics in radiology, nuclear medicine, and medical imaging from 2011–2021: a bibliometric analysis of highly cited papers. Japanese Journal of Radiology, 40(8), 847–856. https://doi.org/10.1007/s11604-022-01268-z
60. Yanbing, S., Ruifang, Z., Chen, W., Shifan, H., Hua, L., & Zhiguang, D. (2020). Bibliometric analysis of the Journal of Nursing Management from 1993 to 2018. Journal of Nursing Management, 28, 317 – 331. https://doi.org/10.1111/jonm.12925
61. Zheltukhina, M. R., Sergeeva, O. V., Masalimova, A. R., Budkevich, R. L., Kosarenko, N. N., & Nesterov, G. V. (2024). A bibliometric analysis of publications on ChatGPT in education: Research patterns and topics. Online Journal of Communication and Media Technologies, 14(1), e202405. https://doi.org/10.30935/ojcmt/14103