Artificial Intelligence on Patients with Cancer in Nursing Studies of the Top 100 Cited Papers: A Bibliometric Analysis and Social Network Analysis
Year: 2026
Type: article
Abstract: 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.
Authors: Merve Gözde Sezgin, Hicran Bektaş
Institution: Akdeniz University
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W7123357753