Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature
Cureus . 2020 Oct 15;12(10):e10961. doi: 10.7759/cureus.10961.
Emre Pakdemirli, Urszula Wegner
Background: Artificial intelligence (AI) has significantly impacted numerous medical specialties with high emphasis on radiology. Associated novel diagnostic methods have become a rapidly emerging hot topic, and it is essential to provide insights into quantitative analysis of the growing literature.
Purpose: The purpose of this study is to highlight future academic trends, identify potential research gaps, and analyze scientific landscape of AI in the field of medicine. The main aim is to explore comprehensive dataset over a 46-year period in terms of publication type, publication citation, country of origin, institution, and medical specialty.
Material and Methods: The Web of Science database was searched from 1975 to 2020, and publications on AI were explored. Both original research reports and review articles were included in comprehensive bibliometric analysis. Descriptive statistics were calculated, and numerous variables were applied, namely year of publication, institution, type of publication, specialty area, country of origin, and citation numbers, and the Kruskal-Wallis analysis of variance was used.
Results: A total of 117,974 relevant citations were retrieved, of which 83,979 original research and review articles were retained for analysis. Not surprisingly, the largest proportion of citations were from the United States (23%, n = 19,180) followed by China, Spain, England, and Germany. The number of citations was relatively consistent during the 1970s and emerging gradually during the 1980s. However, ongoing scientific trend positively evolved, and the numbers started to grow significantly in the 1990s and demonstrated continuous increasing wave since then. The most frequently represented key medical specialties were oncology, radiology, neuroradiology, and ophthalmology. Overall, no major statistical difference was found between these four domains (p = 0.753).
Conclusions: In summary, research on AI-powered technologies in the medical domain was at early stage in the 1970s. However, associated deep learning algorithms significantly attracted and revolutionized the scientific community with subsequent evolution of research and exponential growth of multidisciplinary publications since that time. Work in this field has impacted radiology as an area of predominant interest and has been led by institutions in the United States, Spain, France, China, and England. The bibliometric study reported herein can provide a broad overview and valuable guidance to help medical researchers gain insights into key points and trace the global trends regarding the status of AI research in medicine, particularly in radiology and other relevant multispecialty areas.
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