@article{JMAI5216,
author = {Subhashaan Sreedharan and Mustafa Mian and Ross A. Robertson and Natalie Yang},
title = {The top 100 most cited articles in medical artificial intelligence: a bibliometric analysis},
journal = {Journal of Medical Artificial Intelligence},
volume = {3},
number = {0},
year = {2019},
keywords = {},
abstract = {Background: There is growing interest in the use of artificial intelligence (AI) in medicine. Our objective was to identify and analyse the characteristics of the top 100 most cited articles relating to the use of AI in medicine in order to identify research trends and help direct future research.
Methods: A retrospective bibliometric analysis of the 100 most cited articles relating to the use of AI in medicine between the years 1950 and 2019 was performed. Data extracted for analysis included year of publication, authorship, journal title and impact factor, institution, country of origin, article type, keywords, and field of medicine.
Results: The number of citations for the top 100 articles ranged from 176 to 1,475, with a median [interquartile range (IQR)] of 238 [205–347]. The median [IQR] number of citations per year was 21 [16–41]. The majority of the top 100 articles [85] were published in the last two decades. Over half of the top 100 articles [55] originated from the United States. There were 60 original research articles featured, with 11 of these clinical studies. The most represented fields were medical informatics [25] and radiology [21]. Oncology is pioneering the clinical integration of AI applications, while cardiovascular medicine is lacking in AI research despite its high disease burden.
Conclusions: This study highlights that the current citation classics are largely in the non-clinical, experimental phase and have yet to progress to the clinical, integration phase of medical AI.},
issn = {2617-2496}, url = {https://jmai.amegroups.org/article/view/5216}
}