Global Research Trends, Hotspots, Impacts, and Emergence of Artificial Intelligence and Machine Learning in Health and Medicine: A 25-Year Bibliometric Analysis

Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI and ML in health and medicine. Methods: We used the Scopus database for searching and extracted articles published between 2000 and 2024. Then, we generated information about productivity, citations, collaboration, most impactful research topics, emerging research topics, and author keywords using Microsoft Excel 365 and VOSviewer software (version 1.6.20). Results: We retrieved a total of 22,113 research articles, with a notable surge in research activity in recent years. Core journals were Scientific Reports and IEEE Access, and core institutions included Harvard Medical School and the Ministry of Education of the People's Republic of China, while core countries comprised the United States, China, India, the United Kingdom, and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI's and ML impact on health and medicine. Frequent author keywords identified key research hotspots, including specific diseases like Alzheimer's disease, Parkinson's diseases, COVID-19, and diabetes. The author keyword analysis identified "deep learning", "convolutional neural network", and "classification" as dominant research themes. Conclusions: AI's transformative potential in AI and ML in health and medicine holds promise for improving global health outcomes.