Visualising the Knowledge Domain of Artificial Intelligence in Marketing: A Bibliometric Analysis

2020 ◽  
pp. 43-53
Author(s):  
Elvira Ismagiloiva ◽  
Yogesh Dwivedi ◽  
Nripendra Rana
Keyword(s):  
Artificial Intelligence ◽  
Bibliometric Analysis ◽  
Knowledge Domain

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

scholarly journals Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 441
Author(s):  
Md. Mohaimenul Islam ◽  
Tahmina Nasrin Poly ◽  
Belal Alsinglawi ◽  
Li-Fong Lin ◽  
Shuo-Chen Chien ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bibliometric Analysis ◽  
English Language ◽  
R Package ◽  
Research Area ◽  
Republic Of China ◽  
Comprehensive Picture ◽  
The Usa ◽  
The Republic ◽  
Academy Of Sciences

The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.

scholarly journals Application of the Theory of Planned Behavior in Environmental Science: A Comprehensive Bibliometric Analysis

2019 ◽  
Vol 16 (15) ◽  
pp. 2788 ◽  
Author(s):  
Hongyun Si ◽  
Jian-gang Shi ◽  
Daizhong Tang ◽  
Shiping Wen ◽  
Wei Miao ◽  
...  
Keyword(s):  
Theory Of Planned Behavior ◽  
Bibliometric Analysis ◽  
Planned Behavior ◽  
Environmental Science ◽  
Mainland China ◽  
Science Research ◽  
The United States ◽  
Sustainable Transportation ◽  
Application Framework ◽  
Knowledge Domain

Since the theory of planned behavior (TPB) was proposed by Ajzen in 1985, it has attracted extensive interest and been widely applied worldwide. Although an increasing number of studies have employed the TPB in the domain of environmental science, there have been no attempts to retrospectively analyze existing articles. The current study aimed to holistically understand the application status of the TPB in environmental science from a knowledge domain visualization perspective. A total of 531 journal articles were obtained through the Scopus database to perform a bibliometric analysis and content analysis. The results showed that waste management, green consumption, climate and environment, saving and conservation, and sustainable transportation are the primary research topics; the United States (U.S.), Mainland China, the United Kingdom (UK), and Malaysia are the most productive countries/regions. Moreover, the cross-disciplinary situations, main source journals, and key articles were revealed. Furthermore, the extended factors, integrated theories, major methods, specific groups, and control variables of environmental science research using the TPB were elaborated and integrated into a comprehensive application framework. Constructive criticisms were ultimately discussed. The findings contribute in several ways to help relevant researchers learn about the application of TPB to environmental science and provide new insights and holistic references for further research on environment-related behavior.

Role of artificial intelligence in operations environment: a review and bibliometric analysis

2020 ◽  
Vol 32 (4) ◽  
pp. 869-896 ◽  
Author(s):  
Pavitra Dhamija ◽  
Surajit Bag
Keyword(s):  
Artificial Intelligence ◽  
Bibliometric Analysis ◽  
Operations Management ◽  
Big Data Analytics ◽  
Unique Contribution ◽  
Content Type ◽  
Engineering Research ◽  
Sustainable Supply Chains ◽  
Funding Sources ◽  
Citation Statistics

Purpose“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.Design/methodology/approachAs rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).FindingsResults obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.Originality/valueThe result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

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