Global Research on Big Data in Relation with Artificial Intelligence (A Bibliometric Study: 2008-2019)

Kulaklı, Atik; Osmanaj, Valmira

doi:10.3991/ijoe.v16i02.12617

Cited by 37 publications

(25 citation statements)

References 25 publications

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“…We were able to identify two English-language bibliometric studies [10,11] conducted to date on AI in medicine. Kulakli and Osmanaj [10] conducted a review of research studies on big data in relation to AI published in peer-reviewed journals and indexed in WoS for the period of 2008-2019. Their study covers a wider range than ours and included non-medical fields such as engineering, telecommunications, computer science, and law.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers have written extensively on the benefits of AI applications in medicine, emphasizing the potential of this technology to improve diagnostic accuracy, therapeutic efficacy, and the overall clinical treatment process [6]. AI applications have assisted doctors and other medical professionals in numerous domains including health information systems, geocoding of health data, epidemic and syndromic surveillance, predictive models, decision support, and medical imaging [7][8][9][10][11]. AI systems are capable of providing health professionals with continuous real-time updates on medical information from various sources, including journals, textbooks, clinical practices, and patients, enabling improved patient care and supporting appropriate inferences for health risk assessment and prediction of health outcomes [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature

Pakdemirli

Wegner²

2020

Cureus

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature

Pakdemirli

Wegner²

2020

Cureus

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“…A bibliometric study enables researchers to explore patterns, trends, associations, and scientific developments related to searched domains, along with interrelated fields over publication data. This analysis requires a structured bibliometric database to obtain appropriate data for answering research questions [ 37 - 39 ].…”

Section: Methodsmentioning

confidence: 99%

“…Bibliometrics is also defined as a statistical method to analyze bibliometric publications data over a wide spectrum such as peer-reviewed journal articles, books, conference proceedings, periodicals, reviews, reports, and related reports. There are various analysis methods for a literature review along with bibliometric tools [ 37 , 40 - 43 ]. This approach allows for further obtaining more in-depth understanding of a given topic and its publication trends.…”

Section: Methodsmentioning

confidence: 99%

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Scientific Publication Patterns of Mobile Technologies and Apps for Posttraumatic Stress Disorder Treatment: Bibliometric Co-Word Analysis

Kulaklı¹,

Shubina²

2020

JMIR Mhealth Uhealth

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Background Mobile apps are viewed as a promising opportunity to provide support for patients who have posttraumatic stress disorder (PTSD). The development of mobile technologies and apps shows similar trends in PTSD treatment. Therefore, this emerging research field has received substantial attention. Consequently, various research settings are planned for current and further studies. Objective The aim of this study was to explore the scientific patterns of research domains related to mobile apps and other technologies for PTSD treatment in scholarly publications, and to suggest further studies for this emerging research field. Methods We conducted a bibliometric analysis to identify publication patterns, most important keywords, trends for topicality, and text analysis, along with construction of a word cloud for papers published in the last decade (2010 to 2019). Research questions were formulated based on the relevant literature. In particular, we concentrated on highly ranked sources. Based on the proven bibliometric approach, the data were ultimately retrieved from the Web of Science Core Collection (Clarivate Analytics). Results A total of 64 studies were found concerning the research domains. The vast majority of the papers were written in the English language (63/64, 98%) with the remaining article (1/64, 2%) written in French. The articles were written by 323 authors/coauthors from 11 different countries, with the United States predominating, followed by England, Canada, Italy, the Netherlands, Australia, France, Germany, Mexico, Sweden, and Vietnam. The most common publication type was peer-reviewed journal articles (48/64, 75%), followed by reviews (8/64, 13%), meeting abstracts (5/64, 8%), news items (2/64, 3%), and a proceeding (1/64, 2%). There was a mean of 6.4 papers published per year over the study period. There was a 100% increase in the number of publications published from 2016 to 2019 with a mean of 13.33 papers published per year during this latter period. Conclusions Although the number of papers on mobile technologies for PTSD was quite low in the early period, there has been an overall increase in this research domain in recent years (2016-2019). Overall, these findings indicate that mobile health tools in combination with traditional treatment for mental disorders among veterans increase the efficiency of health interventions, including reducing PTSD symptoms, improving quality of life, conducting intervention evaluation, and monitoring of improvements. Mobile apps and technologies can be used as supportive tools in managing pain, anger, stress, and sleep disturbance. These findings therefore provide a useful overview of the publication trends on research domains that can inform further studies and highlight potential gaps in this field.

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Process Mining Research in Management Science and Engineering Fields: The Period of 2010–2019

Kulaklı

Birgün

2020

Lecture Notes in Mechanical Engineering

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Global Research on Big Data in Relation with Artificial Intelligence (A Bibliometric Study: 2008-2019)

Cited by 37 publications

References 25 publications

Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature

Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature

Scientific Publication Patterns of Mobile Technologies and Apps for Posttraumatic Stress Disorder Treatment: Bibliometric Co-Word Analysis

Process Mining Research in Management Science and Engineering Fields: The Period of 2010–2019

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