The top 100 cited papers in AI and Healthcare: trends, collaborations, and key contributions

Abstract Purpose This study performed a bibliometric analysis of the top 100 most cited papers on artificial intelligence (AI) and its applications in health and medicine. Data was retrieved from the Scopus database in December 2024, focusing on papers published between 2001 and 2024. Methods The search string included terms such as "artificial intelligence," "machine learning," "deep learning," and "electronic health records," combined with "health" or "medicine." To ensure specificity, only documents with these terms in their titles were included. The analysis was conducted in two stages: (1) exploring publications to identify a focused dataset and (2) selecting the top 100 most cited papers for in-depth evaluation. Results A total of 9,219 documents were analyzed, comprising 7,959 articles and 1,260 reviews. D.W. Bates emerged as the top author with 103 publications, Harvard Medical School was the leading institution, with 560 publications, while the United States dominated geographically, contributing 5,413 papers. The publication details, top authors, universities, and countries are presented in supplementary tables. Figures illustrate the dynamics of authors, universities, and countries, as well as collaboration networks for authors, departments, and countries. Additionally, uni-, bi-, and tri-gram analyses and the thematic evolution of the top 100 most cited papers were explored. Conclusions This study provides a detailed analysis of AI applications in health and medicine, highlighting influential authors, institutions, and countries, as well as funding dynamics and publication trends. The findings offer valuable insights into the evolution of research in this domain and its key contributors, serving as a foundation for guiding future investigations and identifying emerging trends.