Trends in artificial intelligence and machine learning for renal cancer

Area of Science:

• Medical Informatics
• Oncology
• Artificial Intelligence

Background:

• Artificial intelligence (AI) and machine learning (ML) are rapidly advancing and finding increasing applications in medical research, particularly in renal cancer (RC).
• These technologies offer significant potential for improving diagnosis, prognosis, and treatment planning in RC by analyzing complex datasets.
• AI and ML facilitate novel approaches to understanding renal cancer.

Purpose of the Study:

• To systematically review and analyze the research landscape of AI and ML in renal cancer.
• To identify key trends, influential institutions, prolific authors, and emerging research areas in this field.
• To understand the collaboration patterns and publication venues within AI and ML-driven RC research.

Main Methods:

• A systematic literature search was conducted using Web of Science Core Collection for studies from 2012 to 2025.
• Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package bibliometrix.
• Analysis focused on co-authorship, institutional collaborations, citations, keyword co-occurrences, and research trends.

Main Results:

• A total of 1,055 articles were identified, demonstrating a 48.45% annual growth rate.
• China leads in publications (36.6%), with the University of Texas System as the most prolific institution.
• Collaboration is largely regional, with limited international engagement. Key authors include Checcucci, E. and Wang, X., and primary journals are Scientific Reports and Frontiers in Oncology.
• Emerging research hotspots include nivolumab, immune-checkpoint inhibitors, tumor microenvironment, and therapy resistance.

Conclusions:

• AI and ML are poised to become increasingly important in renal cancer research.
• Addressing challenges and limitations is crucial for the ethical and successful clinical integration of these technologies.
• Continued research, collaboration, and innovation are necessary to maximize the benefits of AI and ML in oncology.