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Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis

Yıl 2026, Cilt: 9 Sayı: 1, 138 - 149, 13.01.2026

Merve Gözde Sezgin , Hicran Bektaş

https://doi.org/10.38108/ouhcd.1542969

Öz

Objective: This study aimed to identify and analyze the trends and hotspots of the top 100 most cited nursing studies on artificial intelligence in the care of patients with cancer using bibliometric and social network analysis methods.
Methods: The study was conducted using retrospective bibliometric and social network analysis. The data were obtained from the Web of Science database. The search terms were determined to be “artificial intelligence” and “cancer.” The search was then filtered by selecting the nursing category from the Web of Science database. The analysis includes the top 100 most-cited studies. Study data were analyzed in Microsoft Excel, SPSS, and R Studio software (Bibliometrix), and VOSviewer was used.
Results: The first 100 studies included in the analysis were published in 57 different journals between 1991 and 2022. It was found that 503 different authors had produced these studies in 241 institutions. The journal with the highest number of publications was “Teaching and Learning in Nursing”; most were produced in the USA. Most of the citations were made to the publications in “CIN-Computers Informatics Nursing.” The most frequently used keywords were “artificial intelligence,” “cancer,” and “chatbots.” Additionally, author keywords were classified into five groups focusing on “oncological technology,” “cancer management,” “telehealth,” “palliative nursing,” and “electronic learning”.
Conclusion: Our study found that the hotspots and research trends in this field are the keywords of artificial intelligence, chatbot, and cancer in patients with cancer in nursing. Our findings may help researchers, institutions, and health professionals collaborate. In addition, future studies should examine topics such as chatbots and machine learning in this field to deepen the literature and advance nursing care in the field of cancer.

Anahtar Kelimeler

cancer artificial intelligence bibliometric analysis nursing social network analysis

Kaynakça

  • Aapro M, Bossi P, Dasari A, Fallowfield L, Gascón P, Geller M, et al. (2020). Digital health for optimal supportive care in oncology: Benefits, limits, and future perspectives. Supportive Care in Cancer, 28(10), 4589-4612. https://doi.org/10.1007/s00520-020-05539-1
  • Biscaro C, Giupponi C. (2014). Co-authorship and bibliographic coupling network effects on citations. Plos One, 9, 9(6), e99502. https://doi.org/10.1371/ journal.pone.0099502
  • Borgatti SP, Mehra A, Brass DJ, Labianca G. (2009). Network analysis in the social sciences. Science, 13, 323(5916), 892-5. https://doi.org/10.1126/science .1165821
  • Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229-263. https://doi.org/10.3322/caac.21834
  • Chang CY, Jen HJ, Su WS. (2022). Trends in artificial intelligence in nursing: Impacts on nursing management. Journal of Nursing Management, 30, 3644-3653. https://doi.org/10.1111/jonm.13770
  • Cheng L, Liu F, Mao X, Peng W, Wang Y, Huang H, et al. (2022). The pediatric cancer survivors' user experiences with digital health interventions: A systematic review of qualitative data. Cancer Nursing, 45, E68-e82. https://doi.org/10.1097/ncc.00000000 00000885.
  • Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-96. https://doi.org/10.1016/ j.jbusres.2021.04.070
  • Fu J, Cai W, Zeng B, He L, Bao L, Lin Z, et al. (2022). Development and validation of a predictive model for peripherally inserted central catheter-related thrombosis in breast cancer patients based on artificial neural network: A prospective cohort study. International Journal of Nursing Studies, 135, 104341. https://doi.org/10.1016/j.ijnurstu.2022.1043 41
  • Greer S, Ramo D, Chang YJ, Fu M, Moskowitz J, Haritatos J. (2019). Use of the chatbot "vivibot" to deliver positive psychology skills and promote well-being among young people after cancer treatment: Randomized controlled feasibility trial. Journal of Medical Internet Research Mhealth Uhealth, 7, e15018. https://doi.org/10.2196/15018
  • Guleria D, Kaur G. (2021). Bibliometric analysis of ecopreneurship using VOSviewer and RStudio bibliometrix, 1989–2019. Library Hi-Technology, 1(24), 1001-1024. https://doi.org/10.1108/LHT-09-2020-0218.
  • Hegde S, Ajila V, Zhu W, Zeng C. (2022). Artificial intelligence in early diagnosis and prevention of oral cancer. Asia Pacific Journal of Oncology Nursing, 9, 100133. https://doi.org/10.1016/j.apjon.2022.100133
  • Huenteler J, Ossenbrink J, Schmidt TS, Hoffmann VH. (2016). How a product's design hierarchy shapes the evolution of technological knowledge-evidence from patent-citation networks in wind power. Research Policy, 45, 1195-1217. https://doi.org/10.1016/ j.respol.2016.03.014
  • International Agency for Research on Cancer (IRAC), Global Cancer Statistics (GLOBOCAN) (2024). Cancer Today. https://gco.iarc.fr/today/online-analysis-table?v=2018&mode=cancer&mode _population=continents&population=900&populations=900&key=asr&sex=0&cancer=39&type=0&statistic =5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&group_cancer= 1&include_nmsc=1&include_nmsc_other=1
  • Kamei T. (2022). Telenursing and artificial intelligence for oncology nursing. Asia Pacific Journal of Oncology Nursing, 9, 100119. https://doi.org/10.1016/j.apjon.2022.100119
  • Kantek F, Yesilbas H, Yildirim N, Dundar Kavakli B. (2023). Social network analysis: Understanding nurses' advice-seeking interactions. International Journal of Nursing Review, 70(3), 322-328. https://doi.org/10.1111/inr.12763
  • Kuo CC, Wang HH, Tseng LP. (2022). Using data mining technology to predict medication-taking behaviour in women with breast cancer: A retrospective study. Nursing Open, 9, 2646-2656. https://doi.org/10.1002 /nop2.963
  • Li HJ, An HZ, Wang Y, Huang JC, Gao XY. (2016). Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network. Physica a-Statistical Mechanics and Its Applications. 450, 657-669. https://doi.org/10.1016/j.physa.2016 .01.017
  • Li HL, Lin SW, Hwang YT. (2019). Using nursing information and data mining to explore the factors that predict pressure injuries for patients at the end of life. CIN: Computers Informatic Nursing, 37, 133-141. https://doi.org/10.1097/cin.0000000000000489.
  • Marthick M, McGregor D, Alison J, Cheema B, Dhillon H, Shaw T. (2021). Supportive care interventions for people with cancer assisted by digital technology: Systematic review. Journal of Medical Internet Research, 23, e24722. https://doi.org/10.2196/24722
  • Martín-Del-Río B, Solanes-Puchol Á, Martínez-Zaragoza F, Benavides-Gil G. (2018). Stress in nurses: The 100 top-cited papers published in nursing journals. Journal of Advanced Nursing, 74, 1488-1504. https://doi.org/10.1111/jan.13566.
  • Mlakar I, Lin S, Aleksandraviča I, Arcimoviča K, Eglītis J, Leja M, et al. (2021). Patients-centered SurvivorShIp care plan after cancer treatments based on big data and artificial intelligence technologies (PERSIST): A multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors. BMC Medical Informatics Decision Making, 21, 243. https://doi.org/10.1186/s12911-021-01603-w
  • Montero I, Leon OG. (2007). A guide for naming research studies in psychology. International Journal of Clinical and Health Psychology. 7:847-862.
  • Montinaro V, Giliberti M, Villani C, Montinaro A. (2019). Citation classics: Ranking of the top 100 most cited articles in nephrology. Clinical Kidney Journal, 12, 6-18. https://doi.org/10.1093/ckj/sfy033
  • Pan LC, Wu XR, Lu Y, Zhang HQ, Zhou YL, Liu X, et al. (2022). Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review. Asia Pacific Journal of Oncology Nursing, 9, 100127. https://doi.org/10.1016/j.apjon. 2022.100127
  • Pena-Ibanez F, Ruiz-Iniguez R. (2019). The most cited articles in Spanish nursing (1997-2016): A bibliometric analysis. Rqr Enfermeria Comunitaria. 7, 5-25.
  • Ruland CM, Andersen T, Jeneson A, Moore S, Grimsbø GH, Børøsund E, et al. (2013). Effects of an internet support system to assist cancer patients in reducing symptom distress: A randomized controlled trial. Cancer Nursing, 36, 6-17. https://doi.org/10.1097/ NCC.0b013e31824d90d4
  • Shen Z, Wu H, Chen Z, Hu J, Pan J, Kong J, et al. (2022). The Global research of artificial intelligence on prostate cancer: A 22-year bibliometric analysis. Frontiers in Oncology, 12, 843735. https://doi.org/10.3389/fonc.2022.843735
  • Shi J, Wei S, Gao Y, Mei F, Tian J, Zhao Y, et al. (2022). Global output on artificial intelligence in the field of nursing: A bibliometric analysis and science mapping. Journal of Nursing Scholarship, 55(4), 853-863. https://doi.org/https://doi.org/10.1111/jnu.12852
  • Stavropoulou C, Somai M, Ioannidis JPA. (2019). Most UK scientists who publish extremely highly-cited papers do not secure funding from major public and charity funders: A descriptive analysis. Plos One, 14. https://doi.org/10.1371/journal.pone.0211460.
  • Tahamtan I, Afshar AS, Ahamdzadeh K. (2016). Factors affecting number of citations: A comprehensive review of the literature. Scientometrics, 07, 1195-1225. https://doi.org/10.1007/s11192-016-1889-2
  • Tofthagen C, Kip KE, Passmore D, Loy I, Berry DL. (2016). Usability and acceptability of a web-based program for chemotherapy-induced peripheral neuropathy. CIN: Computers, Informatics, Nursing, 34, 322-329. https://doi.org/10.1097/cin.00000000 00000242.
  • Uthman OA, Okwundu CI, Wiysonge CS, Young T, Clarke A. (2013). Citation classics in systematic reviews and meta-analyses: Who wrote the top 100 most cited articles? Plos One, 8. https://doi.org/10.1371/journal.pone.0078517
  • Wang L. (2022). Predicting colorectal cancer using residual deep learning with nursing care. Contrast Media & Molecular Imaging, 7996195. https://doi.org/10.1155/2022/7996195
  • Weingart P. (2005). Impact of bibliometrics upon the science system: Inadvertent consequences? Scientometrics, 62, 117-131. https://doi.org/10.1007/ s11192-005-0007-7
  • Wilson CM, Mooney K. (2020). Advancing oncology nursing practice through the adoption of patient monitoring digital tools. Seminars in Oncology Nursing, 36, 151087. https://doi.org/10.1016/ j.soncn. 2020.151087
  • World Health Organization (WHO). (2021). Global strategy on digital health 2020-2025. https://www.who.int/ docs/default-source/ documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf?sfvrsn=f112ede5_58
  • Wu Y, Zhao Y, Lin L, Lu Z, Guo Z, Li X, et al. (2018). Fifty top-cited spine articles from mainland China: A citation analysis. Journal of Medical Internet Research, 46, 773-784. https://doi.org/10.1177/ 0300060517713804
  • Zavadskas E, Skibniewski M, 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. https://doi.org/10.1016/j.acme.2014.05.008
  • Zhang Y, Yu C, Zhao F, Xu H, Zhu C, Li Y. (2022). Landscape of artificial intelligence in breast cancer (2000-2021): A bibliometric analysis. Frontiers in Bioscience-Landmark, 27, 224. https://doi.org/10. 31083/j.fbl2708224
  • Zhu J, Song LJ, Zhu L, Johnson RE. (2019). Visualizing the landscape and evolution of leadership research. The Leadership Quarterly, 30(2), 215-232. https://doi.org/10.1016/J.LEAQUA.2018.06.003

Hemşirelik Çalışmalarında Kanser Hastalarında Yapay Zekâ Üzerine En Çok Atıf Alan 100 Makale: Bibliyometrik Analiz ve Sosyal Ağ Analizi

Yıl 2026, Cilt: 9 Sayı: 1, 138 - 149, 13.01.2026

Merve Gözde Sezgin , Hicran Bektaş

https://doi.org/10.38108/ouhcd.1542969

Öz

Amaç: Bu çalışma, bibliyometrik ve sosyal ağ analizi yöntemlerini kullanarak kanser hastalarının bakımında yapay zeka üzerine en çok atıf alan ilk 100 hemşirelik çalışmasının eğilimlerini ve popüler noktalarını belirlemeyi ve analiz etmeyi amaçlamıştır.
Yöntem: Çalışma, retrospektif bibliyometrik ve sosyal ağ analizi kullanılarak yürütülmüştür. Veriler Web of Science veritabanından elde edilmiştir. Arama terimleri “yapay zeka” ve “kanser” olarak belirlenmiştir. Daha sonra arama Web of Science’tan hemşirelik kategorisi seçilerek filtrelenmiştir. Analiz en çok atıf alan ilk 100 çalışmayı içermektedir. Çalışma verileri Microsoft Excel, SPSS ve R Studio yazılımlarında (Bibliometrix) analiz edilmiş ve VOSviewer kullanılmıştır.
Bulgular: Analize dahil edilen ilk 100 çalışma, 1991 ile 2022 yılları arasında 57 farklı dergide yayınlanmıştır. Bu çalışmaların 241 kurumda 503 farklı yazarın ürettiği bulunmuştur. En fazla yayın sayısına sahip dergi “Teaching and Learning in Nursing” olmuştur; çoğu ABD'de üretilmiştir. Atıfların çoğu “CIN-Computers Informatics Nursing” dergisindeki yayınlara yapılmıştır. En sık kullanılan anahtar kelimeler “yapay zeka”, “kanser” ve “sohbet robotu” olarak belirlenmiştir. Ayrıca, yazar anahtar kelimeleri "onkolojik teknoloji," "kanser yönetimi," "tele sağlık", "palyatif hemşirelik" ve "elektronik öğrenme" üzerine odaklanarak beş gruba ayrılmıştır.
Sonuç: Çalışmamızın sonucunda, bu alanın popüler noktalarının ve araştırma eğilimlerinin hemşirelikte kanser hastalarında yapay zeka, chatbot ve kanser anahtar kelimeleri olduğu bulunmuştur. Çalışma bulgularımızın, araştırmacıların, kurumların ve sağlık profesyonellerinin iş birliği yapabilmesinde yardımcı olabileceği düşünülmektedir. Ayrıca, bu alanda chatbot ve makine öğrenimi gibi konuların gelecekteki çalışmalar tarafından incelenmesinin literatürün derinleşmesine ve kanser alanında hemşirelik bakımının ilerlemesine katkı sağlayacağı düşünülmektedir.

Anahtar Kelimeler

kanser yapay zeka bibliyometrik analiz hemşirelik sosyal ağ analizi

Kaynakça

  • Aapro M, Bossi P, Dasari A, Fallowfield L, Gascón P, Geller M, et al. (2020). Digital health for optimal supportive care in oncology: Benefits, limits, and future perspectives. Supportive Care in Cancer, 28(10), 4589-4612. https://doi.org/10.1007/s00520-020-05539-1
  • Biscaro C, Giupponi C. (2014). Co-authorship and bibliographic coupling network effects on citations. Plos One, 9, 9(6), e99502. https://doi.org/10.1371/ journal.pone.0099502
  • Borgatti SP, Mehra A, Brass DJ, Labianca G. (2009). Network analysis in the social sciences. Science, 13, 323(5916), 892-5. https://doi.org/10.1126/science .1165821
  • Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229-263. https://doi.org/10.3322/caac.21834
  • Chang CY, Jen HJ, Su WS. (2022). Trends in artificial intelligence in nursing: Impacts on nursing management. Journal of Nursing Management, 30, 3644-3653. https://doi.org/10.1111/jonm.13770
  • Cheng L, Liu F, Mao X, Peng W, Wang Y, Huang H, et al. (2022). The pediatric cancer survivors' user experiences with digital health interventions: A systematic review of qualitative data. Cancer Nursing, 45, E68-e82. https://doi.org/10.1097/ncc.00000000 00000885.
  • Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-96. https://doi.org/10.1016/ j.jbusres.2021.04.070
  • Fu J, Cai W, Zeng B, He L, Bao L, Lin Z, et al. (2022). Development and validation of a predictive model for peripherally inserted central catheter-related thrombosis in breast cancer patients based on artificial neural network: A prospective cohort study. International Journal of Nursing Studies, 135, 104341. https://doi.org/10.1016/j.ijnurstu.2022.1043 41
  • Greer S, Ramo D, Chang YJ, Fu M, Moskowitz J, Haritatos J. (2019). Use of the chatbot "vivibot" to deliver positive psychology skills and promote well-being among young people after cancer treatment: Randomized controlled feasibility trial. Journal of Medical Internet Research Mhealth Uhealth, 7, e15018. https://doi.org/10.2196/15018
  • Guleria D, Kaur G. (2021). Bibliometric analysis of ecopreneurship using VOSviewer and RStudio bibliometrix, 1989–2019. Library Hi-Technology, 1(24), 1001-1024. https://doi.org/10.1108/LHT-09-2020-0218.
  • Hegde S, Ajila V, Zhu W, Zeng C. (2022). Artificial intelligence in early diagnosis and prevention of oral cancer. Asia Pacific Journal of Oncology Nursing, 9, 100133. https://doi.org/10.1016/j.apjon.2022.100133
  • Huenteler J, Ossenbrink J, Schmidt TS, Hoffmann VH. (2016). How a product's design hierarchy shapes the evolution of technological knowledge-evidence from patent-citation networks in wind power. Research Policy, 45, 1195-1217. https://doi.org/10.1016/ j.respol.2016.03.014
  • International Agency for Research on Cancer (IRAC), Global Cancer Statistics (GLOBOCAN) (2024). Cancer Today. https://gco.iarc.fr/today/online-analysis-table?v=2018&mode=cancer&mode _population=continents&population=900&populations=900&key=asr&sex=0&cancer=39&type=0&statistic =5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&group_cancer= 1&include_nmsc=1&include_nmsc_other=1
  • Kamei T. (2022). Telenursing and artificial intelligence for oncology nursing. Asia Pacific Journal of Oncology Nursing, 9, 100119. https://doi.org/10.1016/j.apjon.2022.100119
  • Kantek F, Yesilbas H, Yildirim N, Dundar Kavakli B. (2023). Social network analysis: Understanding nurses' advice-seeking interactions. International Journal of Nursing Review, 70(3), 322-328. https://doi.org/10.1111/inr.12763
  • Kuo CC, Wang HH, Tseng LP. (2022). Using data mining technology to predict medication-taking behaviour in women with breast cancer: A retrospective study. Nursing Open, 9, 2646-2656. https://doi.org/10.1002 /nop2.963
  • Li HJ, An HZ, Wang Y, Huang JC, Gao XY. (2016). Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network. Physica a-Statistical Mechanics and Its Applications. 450, 657-669. https://doi.org/10.1016/j.physa.2016 .01.017
  • Li HL, Lin SW, Hwang YT. (2019). Using nursing information and data mining to explore the factors that predict pressure injuries for patients at the end of life. CIN: Computers Informatic Nursing, 37, 133-141. https://doi.org/10.1097/cin.0000000000000489.
  • Marthick M, McGregor D, Alison J, Cheema B, Dhillon H, Shaw T. (2021). Supportive care interventions for people with cancer assisted by digital technology: Systematic review. Journal of Medical Internet Research, 23, e24722. https://doi.org/10.2196/24722
  • Martín-Del-Río B, Solanes-Puchol Á, Martínez-Zaragoza F, Benavides-Gil G. (2018). Stress in nurses: The 100 top-cited papers published in nursing journals. Journal of Advanced Nursing, 74, 1488-1504. https://doi.org/10.1111/jan.13566.
  • Mlakar I, Lin S, Aleksandraviča I, Arcimoviča K, Eglītis J, Leja M, et al. (2021). Patients-centered SurvivorShIp care plan after cancer treatments based on big data and artificial intelligence technologies (PERSIST): A multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors. BMC Medical Informatics Decision Making, 21, 243. https://doi.org/10.1186/s12911-021-01603-w
  • Montero I, Leon OG. (2007). A guide for naming research studies in psychology. International Journal of Clinical and Health Psychology. 7:847-862.
  • Montinaro V, Giliberti M, Villani C, Montinaro A. (2019). Citation classics: Ranking of the top 100 most cited articles in nephrology. Clinical Kidney Journal, 12, 6-18. https://doi.org/10.1093/ckj/sfy033
  • Pan LC, Wu XR, Lu Y, Zhang HQ, Zhou YL, Liu X, et al. (2022). Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review. Asia Pacific Journal of Oncology Nursing, 9, 100127. https://doi.org/10.1016/j.apjon. 2022.100127
  • Pena-Ibanez F, Ruiz-Iniguez R. (2019). The most cited articles in Spanish nursing (1997-2016): A bibliometric analysis. Rqr Enfermeria Comunitaria. 7, 5-25.
  • Ruland CM, Andersen T, Jeneson A, Moore S, Grimsbø GH, Børøsund E, et al. (2013). Effects of an internet support system to assist cancer patients in reducing symptom distress: A randomized controlled trial. Cancer Nursing, 36, 6-17. https://doi.org/10.1097/ NCC.0b013e31824d90d4
  • Shen Z, Wu H, Chen Z, Hu J, Pan J, Kong J, et al. (2022). The Global research of artificial intelligence on prostate cancer: A 22-year bibliometric analysis. Frontiers in Oncology, 12, 843735. https://doi.org/10.3389/fonc.2022.843735
  • Shi J, Wei S, Gao Y, Mei F, Tian J, Zhao Y, et al. (2022). Global output on artificial intelligence in the field of nursing: A bibliometric analysis and science mapping. Journal of Nursing Scholarship, 55(4), 853-863. https://doi.org/https://doi.org/10.1111/jnu.12852
  • Stavropoulou C, Somai M, Ioannidis JPA. (2019). Most UK scientists who publish extremely highly-cited papers do not secure funding from major public and charity funders: A descriptive analysis. Plos One, 14. https://doi.org/10.1371/journal.pone.0211460.
  • Tahamtan I, Afshar AS, Ahamdzadeh K. (2016). Factors affecting number of citations: A comprehensive review of the literature. Scientometrics, 07, 1195-1225. https://doi.org/10.1007/s11192-016-1889-2
  • Tofthagen C, Kip KE, Passmore D, Loy I, Berry DL. (2016). Usability and acceptability of a web-based program for chemotherapy-induced peripheral neuropathy. CIN: Computers, Informatics, Nursing, 34, 322-329. https://doi.org/10.1097/cin.00000000 00000242.
  • Uthman OA, Okwundu CI, Wiysonge CS, Young T, Clarke A. (2013). Citation classics in systematic reviews and meta-analyses: Who wrote the top 100 most cited articles? Plos One, 8. https://doi.org/10.1371/journal.pone.0078517
  • Wang L. (2022). Predicting colorectal cancer using residual deep learning with nursing care. Contrast Media & Molecular Imaging, 7996195. https://doi.org/10.1155/2022/7996195
  • Weingart P. (2005). Impact of bibliometrics upon the science system: Inadvertent consequences? Scientometrics, 62, 117-131. https://doi.org/10.1007/ s11192-005-0007-7
  • Wilson CM, Mooney K. (2020). Advancing oncology nursing practice through the adoption of patient monitoring digital tools. Seminars in Oncology Nursing, 36, 151087. https://doi.org/10.1016/ j.soncn. 2020.151087
  • World Health Organization (WHO). (2021). Global strategy on digital health 2020-2025. https://www.who.int/ docs/default-source/ documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf?sfvrsn=f112ede5_58
  • Wu Y, Zhao Y, Lin L, Lu Z, Guo Z, Li X, et al. (2018). Fifty top-cited spine articles from mainland China: A citation analysis. Journal of Medical Internet Research, 46, 773-784. https://doi.org/10.1177/ 0300060517713804
  • Zavadskas E, Skibniewski M, 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. https://doi.org/10.1016/j.acme.2014.05.008
  • Zhang Y, Yu C, Zhao F, Xu H, Zhu C, Li Y. (2022). Landscape of artificial intelligence in breast cancer (2000-2021): A bibliometric analysis. Frontiers in Bioscience-Landmark, 27, 224. https://doi.org/10. 31083/j.fbl2708224
  • Zhu J, Song LJ, Zhu L, Johnson RE. (2019). Visualizing the landscape and evolution of leadership research. The Leadership Quarterly, 30(2), 215-232. https://doi.org/10.1016/J.LEAQUA.2018.06.003
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Dahili Hastalıklar Hemşireliği
Bölüm Araştırma Makalesi
Yazarlar

Merve Gözde Sezgin 0000-0001-9076-2735

Hicran Bektaş 0000-0002-3356-3120

Gönderilme Tarihi 3 Eylül 2024
Kabul Tarihi 13 Kasım 2024
Yayımlanma Tarihi 13 Ocak 2026
Yayımlandığı Sayı Yıl 2026 Cilt: 9 Sayı: 1

Kaynak Göster

APA Sezgin, M. G., & Bektaş, H. (2026). Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis. Ordu Üniversitesi Hemşirelik Çalışmaları Dergisi, 9(1), 138-149. https://doi.org/10.38108/ouhcd.1542969
AMA Sezgin MG, Bektaş H. Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis. Ordu University J Nurs Stud. Ocak 2026;9(1):138-149. doi:10.38108/ouhcd.1542969
Chicago Sezgin, Merve Gözde, ve Hicran Bektaş. “Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis”. Ordu Üniversitesi Hemşirelik Çalışmaları Dergisi 9, sy. 1 (Ocak 2026): 138-49. https://doi.org/10.38108/ouhcd.1542969.
EndNote Sezgin MG, Bektaş H (01 Ocak 2026) Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis. Ordu Üniversitesi Hemşirelik Çalışmaları Dergisi 9 1 138–149.
IEEE M. G. Sezgin ve H. Bektaş, “Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis”, Ordu University J Nurs Stud, c. 9, sy. 1, ss. 138–149, 2026, doi: 10.38108/ouhcd.1542969.
ISNAD Sezgin, Merve Gözde - Bektaş, Hicran. “Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis”. Ordu Üniversitesi Hemşirelik Çalışmaları Dergisi 9/1 (Ocak2026), 138-149. https://doi.org/10.38108/ouhcd.1542969.
JAMA Sezgin MG, Bektaş H. Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis. Ordu University J Nurs Stud. 2026;9:138–149.
MLA Sezgin, Merve Gözde ve Hicran Bektaş. “Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis”. Ordu Üniversitesi Hemşirelik Çalışmaları Dergisi, c. 9, sy. 1, 2026, ss. 138-49, doi:10.38108/ouhcd.1542969.
Vancouver Sezgin MG, Bektaş H. Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis. Ordu University J Nurs Stud. 2026;9(1):138-49.