Emerging trends in artificial intelligence research in lymphedema: An evaluation in light of bibliometric and altmetric data

Abstract

Background

This study aims to systematically evaluate the current landscape of artificial intelligence (AI) and machine learning applications in lymphedema research by employing bibliometric and altmetric analyses. The goal is to identify major trends, research focuses, and influential contributors in this rapidly evolving field.

Method

A total of 43 AI-related articles on lymphedema published between 1975 and 2025 were retrieved from the Web of Science Core Collection. Bibliometric indicators such as publication years, journals, countries, authorship, and citation metrics were analyzed. Altmetric scores were also assessed. Each study was classified by study type and thematic focus.

Result

Original research articles constituted the majority (n = 26), with clinical studies being the most common subtype. The United States and China led in publication output. Most studies were published in Q1-Q2 journals, indicating high scientific quality. Scientific Reports was the most productive journal. General AI applications and risk prediction emerged as dominant themes. A moderate positive correlation was found between average annual citations and altmetric scores (r = 0.470, p = 0.039), suggesting consistency between academic impact and online visibility.

Conclusion

This is the first study to comprehensively map AI-based research in the field of lymphedema using bibliometric and altmetric methods. The findings reveal increasing global interest and high-impact publications, particularly in the domains of risk prediction and early diagnosis. These insights may guide future methodological frameworks and interdisciplinary collaborations in this emerging field.