Bibliometric Insights: How Machine Learning Shapes the Future of Geriatric Care Globally? (Preprint)

BACKGROUND The rapid growth of elderly populations worldwide puts significant pressure on healthcare systems. Simultaneously, advancements in machine learning (ML) provide significant prospects to address several challenges in geriatric care. Comprehensively mapping out the research landscape is crucial to guide responsible expansion of machine learning in this domain. OBJECTIVE This study employs bibliometric analysis to examine the state of research on machine learning applications in geriatric care. It addresses fundamental inquiries regarding growth patterns, influential contributors, subject interconnections, and strategy orientations. METHODS Methodical searches obtained 866 articles from Scopus and Web of Science databases. Trends, productivity metrics, citation examination, and keyword co-occurrence mapping offered a multidimensional view. RESULTS Publications exploded after 2018, spearheaded by the US and China. Prolific scholars like Dr. Yingyao Wang lead high-impact research. Studies showcase ML's potential for early detection, well-timed interventions, and holistic evaluations. Sensors are at the forefront of driving developments in various journals. The thematic analysis reveals emerging patterns such as the increasing prominence of deep learning and the growing importance of ethical considerations. CONCLUSIONS This report offers empirical insights into the rapid and increasing growth, as well as the developing paths, that are driving ML research with the aim of revolutionizing geriatric care. It reveals significant possibilities as well as areas of research that need to be addressed, along with ethical dilemmas that require careful consideration. It will be crucial to maintain a cooperative and interdisciplinary research environment with the goal of promoting fair progress in machine learning. In general, researchers can intentionally utilize the potential of machine learning to significantly improve care for elderly populations. CLINICALTRIAL null