A Bibliometric Analysis of Artificial Intelligence and Machine Learning Trends in Chronic Disease Management
TL;DR
A bibliometric analysis of the scientific literature on AI and ML applications in chronic disease shows an acceleration in research growth and the application of numerous AI approaches in various fields of chronic disease, however, there is a concentration of study and activity around some diseases and countries.
Abstract
Chronic diseases represent one of the most critical fields in healthcare systems, driving the majority of global deaths and healthcare costs. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have increasingly shown their potential in disease diagnosis, prediction and management. In this study, we conducted a bibliometric analysis of the scientific literature on AI and ML applications in chronic disease. We applied a structured multi-criteria selection process in different steps to retain 566 publications for analysis. Publication trends, geographic distribution, journals, and keyword cooccurrence patterns were examined using multiple tools to analyse the retrieved documents. Results show an acceleration in research growth and the application of numerous AI approaches in various fields of chronic disease. However, we found a concentration of study and activity around some diseases and countries. These findings provide a consolidated overview of current research dynamics and establish a foundation for future investigations and foster more balanced international collaboration in chronic disease management.