A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram - Muhammad Arslan Ul Hassan; Sana Mushtaq; Abdul Rehman; Mohammed Abdulkarem Al-Qaisi; Zhen Yang (The Egyptian Heart Journal)
Abstract Background Artificial intelligence (AI) is a modern tool that increases the diagnostic precision of the classical electrocardiogram (ECG). The objective of this bibliometric analysis was to identify the 50 most cited articles in the domain of AI in ECG, emphasizing publication trends, citation metrics, prominent authors and journals, leading institutions, and significant contributing countries. Results The 50 most cited articles on AI in ECG were published between 2000 and 2020 across 25 journals. The mean citations per article were 488.0, with the highest citations count being 1870. ‘IEEE Transactions on Biomedical Engineering’ and ‘Computers in Biology and Medicine’ published the highest number of articles, while Rajendra Acharya U and RS Tan were the most contributing authors. The USA and China had a total of 14 publications, and Singapore was the country with most collaborations. Conclusions This bibliometric analysis provides clinicians and researchers with an overview of evolution and progression of AI in the domain of ECG. Improved collaborations among different countries and institutions are essential for achieving advancements in the utilization of AI in ECG.