The top 100 most cited articles on artificial intelligence in radiology: a bibliometric analysis. Issue 2 (February 2023) - Hughes, H.; O'Reilly, M.; McVeigh, N.; Ryan, R.
Abstract : Aim: To identify the most influential publications relating to artificial intelligence (AI) in radiology in order to identify current trends in the literature and to highlight areas requiring further research. Materials and methods: A retrospective bibliometric analysis was performed of the top 100 most cited articles on this topic. Data pertaining to year of publication, publishing journal, journal impact factor, authorship, article title, institution, country, type of article, article subject, and keywords were collected. Results: The number of citations per article for the top 100 list ranged from 254 to 3, 576 (median 353). The number of citations per year, per article ranged from 10.4 to 894 (median 65.6). The majority of articles ( n= 62) were published within the last 10 years. The USA was the most common country of origin ( n= 44). The journal with the greatest number of articles was IEEE Transactions On Medical Imaging ( n= 38). University Medical Center Utrecht contributed the greatest number of articles ( n= 6). There were 92 original research articles, 52 of which were clinical studies. The most common clinical subjects were neuroimaging ( n= 25) and oncology ( n= 16). The most common keyword used was "deep learning" ( n= 34). Conclusion: This study provides an in-depth analysis of the top 100 most-cited papers on the use of AI in radiology. It also provides researchers with detailed insight into the current influential papers in this field, the characteristics of those studies, as well as potential future trends in this fast-developing area of radiology. Highlights: Significant advances have been made in the field of artificial intelligence (AI). A retrospective bibliometric analysis on the use of AI in radiology was performed. This article details the top 100 most-cited papers on the use of AI in radiology. It provides insight in to the influential papers in this field. It also provides insight in to potential future trends in AI in radiology.