Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR

Area of Science:

• Nephrology
• Artificial Intelligence
• Bibliometrics

Background:

• Artificial intelligence (AI) significantly impacts various scientific fields, including nephrology.
• Analyzing 30 years of AI trends in nephrology research identifies key areas of interest and future directions.

Purpose of the Study:

• To conduct a bibliometric analysis of artificial intelligence (AI) research in nephrology.
• To identify trends, influential publications, and emerging topics in AI-driven nephrology research over three decades.

Main Methods:

• Systematic literature search of the Web of Knowledge database (1990-2019).
• Keywords included AI, deep learning, machine learning, and kidney/renal.
• Citation analysis of the top 50 most cited articles.

Main Results:

• The top 50 cited articles (1990-2019) received 1188 citations.
• Major research topics included kidney tumors, acute kidney injury, dialysis, and kidney transplantation.
• Research output significantly increased after 2010, with the USA as the leading contributor.

Conclusions:

• Interest and achievements in AI for nephrology have grown substantially, particularly after 2010.
• Current AI applications leverage readily available data like radiological images and lab results.
• Future AI integration in nephrology will benefit from genetic and personalized data for enhanced applications.