Systematic bibliometric and visualized analysis of global research trends, impact, emerging areas, and hotspots of artificial intelligence in personalized medicine.

Abstract

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of medicine. An increasing amount of evidence supports their use in personalized medicine research. This trend necessitates a thorough review of the growing literature to assist researchers in understanding the subject. This study aims to comprehensively analyze and systematically chart the research trends, influence, emerging areas, and key hotspots related to AI and ML in personalized medicine literature. The bibliometric and visualized analysis was conducted systematically using the data taken from the Scopus database. Bibliometric indicators were assessed using Microsoft Excel 365, VOSviewer, and the Bibliometrix R package. A total of 3719 articles were identified, accumulating 88,351 citations with a 42.1% annual growth rate. The yearly publication findings reveal notable upward trends over the last 19 years, peaking in 2024. The USA led in publication volume (38.8%). Harvard Medical School was a top institution. Leading researchers in this field are Michael R. Kosorok (20 articles). Journal of Personalized Medicine ranks highest among articles (69 articles). The authors' keyword analysis identified "deep learning," "biomarkers," and "radiomics" as hot research topics. The field of personalized medicine is moving revolutionarily, with AI and ML solutions paving their way and resulting in more research collaboration globally and advancing methodologies at a rapid pace. This study offers a broad knowledge framework, emphasizing significant developments and future directions. The findings offer valuable insights for researchers, policymakers, and funding bodies to support interdisciplinary collaborations and future innovation in AI-driven personalized healthcare.