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Trends in Research Publication Topics Related to Artificial Intelligence for Medicine in Medical Education: A Bibliometric Analysis
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Technology application in healthcare is a recent field devoted to sustainability in the healthcare industry. However, research in this sector has grown at a rapid pace. While this expansion has been advantageous for the discipline, it has also made it more difficult to grasp its extent. As a result, answering questions such as the most important emerging trends in technology for sustainable healthcare research, the most critical breakthrough papers, the influence of these papers, and the most productive and leading researchers have become challenging. Finally, understanding the intellectual framework of the knowledge base on Technology for Sustainable Healthcare (TSH) is also difficult. This study attempted to address these issues by presenting an overview of research work in TSH and, in doing so, attempted to answer some of the previously listed problems. The PRISMA model, along with the science mapping review process using bibliometric analysis tools such as VOSviewer and Python, was employed to analyze published works in healthcare technology indexed in the Scopus database over a span of 24 years. Although research in TSH had been progressing rapidly before the COVID-19 pandemic, the current accelerated shift in TSH research over the past four years may be attributed to the pandemic itself as well as advancements in technologies such as artificial intelligence, machine learning, and the Internet of Things. We also discuss current research themes, prolific authors, institutions, journals, and relationships among published works in TSH. Finally, we present the challenges and prospects of TSH research. The findings of our study would be helpful to researchers working in technology for sustainable healthcare.
The new decade has been witnessing the wide acceptance of artificial intelligence (AI) in education, followed by serious concerns about its ethics. This study examined the essence and principles of AI ethics used in education, as well as the bibliometric analysis of AI ethics for educational purposes. The clustering techniques of VOSviewer (n = 880) led the author to reveal the top 10 authors, sources, organizations, and countries in the research of AI ethics in education. The analysis of clustering solution through CitNetExplorer (n = 841) concluded that the essence of AI ethics for educational purposes included deontology, utilitarianism, and virtue, while the principles of AI ethics in education included transparency, justice, fairness, equity, non-maleficence, responsibility, and privacy. Future research could consider the influence of AI interpretability on AI ethics in education because the ability to interpret the AI decisions could help judge whether the decision is consistent with ethical criteria.
As the world population is expected to touch 9.73 billion by 2050, according to the Food and Agriculture Organization (FAO), the demand for agricultural needs is increasing proportionately. Smart Agriculture is replacing conventional farming systems, employing advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to ensure higher productivity and precise agriculture management to overcome food demand. In recent years, there has been an increased interest in researchers within Smart Agriculture. Previous literature reviews have also conducted similar bibliometric analyses; however, there is a lack of research in Operations Research (OR) insights into Smart Agriculture. This paper conducts a Bibliometric Analysis of past research work in OR knowledge which has been done over the last two decades in Agriculture 4.0, to understand the trends and the gaps. Biblioshiny, an advanced data mining tool, was used in conducting bibliometric analysis on a total number of 1,305 articles collected from the Scopus database between the years 2000–2022. Researchers and decision makers will be able to visualize how newer advanced OR theories are being applied and how they can contribute toward some research gaps highlighted in this review paper. While governments and policymakers will benefit through understanding how Unmanned Aerial Vehicles (UAV) and robotic units are being used in farms to optimize resource allocation. Nations that have arid climate conditions would be informed how satellite imagery and mapping can assist them in detecting newer irrigation lands to assist their scarce agriculture resources.
VOSviewer (version 1.6.17, July 22, 2021). Centre for Science and Technology Studies, Leiden University, The Netherlands. https://www.vosviewer.com; free, donations accepted. Bibliometrix (version 3.1, Sep 24, 2021). Department of Economics and Statistics, University of Naples Federico II, Italy. info@bibliometrix.org; https://www.bibliometrix.org/; free, donations accepted.
Background
In recent years, the rapid development of artificial intelligence (AI) technology has created a new diagnostic and therapeutic opportunity for colorectal cancer (CRC). Numerous academic and clinical studies have demonstrated that high-level auxiliary diagnosis and treatment systems based on AI technology can significantly improve the readability of medical data, objectively provide a reliable and comprehensive reference for physicians, reduce the experience gap between physicians, and aid physicians in making more accurate diagnosis decisions. In this study, we used bibliometric techniques to visually analyze the literature about AI in the CRC field and summarize the current situation and research hotspots in this field.
Methods
The relevant literature on AI in the field of CRC research was obtained from the Web of Science Core Collection (WoSCC) database. The software CiteSpace was utilized to analyze the number of papers, countries, institutions, authors, journals, cited literature, and keywords of the included literature and generate a visual knowledge map. The present study aims to evaluate the origin, current hotspots, and research trends of AI in CRC using bibliometric analysis.
Results
As of March 2022, 64 nations/regions, 230 institutions, 245 journals, and 300 authors had published 562 AI-related articles in the field of CRC. Since 2016, each year has seen an exponential increase. China and the United States were the largest contributors, with the largest number of beneficial research institutions and the closest collaboration relationship. The World Journal of Gastroenterology is this field’s most widely published journal. Diagnosis and treatment research, gene and immunology research, intestinal polyp research, tumor grading research, gastrointestinal endoscopy research, and prognosis research comprised the six topics derived from high-frequency keyword cluster analysis.
Conclusion
In recent years, field research has been a popular topic of discussion. The results of our bibliometric analysis allow us to comprehend better the current situation and trend of this research field, and the quantitative data indicators can serve as a guide for the research and application of global scholars.
Background:
Breast cancer remains one of the leading malignancies in women with distinct clinical heterogeneity and intense multidisciplinary cooperation. Remarkable progresses have been made in artificial intelligence (AI). A bibliometric analysis was taken to characterize the current picture of development of AI in breast cancer.
Materials and methods:
Search process was performed in the Web of Science Core Collection database with analysis and visualization performed by R software, VOSviewer, CiteSpace and Gephi. Latent Dirichlet Allocation (LDA), a machine learning based algorithm, was used for analysis of topic terms.
Results:
A total of 511 publications in the field of AI in breast cancer were retrieved between 2000 to 2021. A total of 103 publications were from USA with 2482 citations, making USA the leading country in the field of AI in breast cancer, followed by China. Mem Sloan Kettering Canc Ctr, Radboud Univ Nijmegen, Peking Univ, Sichuan Univ, ScreenPoint Med BV, Lund Univ, Duke Univ, Univ Chicago, Harvard Med Sch and Univ Texas MD Anderson Canc Ctr were the leading institutions in the field of AI in breast cancer. AI, breast cancer and classification, mammography were the leading keywords. LDA topic modeling identified top fifty topics relating the AI in breast cancer. A total of five primary clusters were found within the network of fifty topics, including radiology feature, lymph node diagnosis and model, pathological tissue and image, dataset classification and machine learning, gene expression and survival.
Conclusions:
This research depicted AI studies in breast cancer and presented insightful topic terms with future perspective.
Through the recent technological developments within the fourth industrial revolution, artificial intelligence (AI) studies have had a huge impact on various disciplines such as social sciences, information communication technologies (ICTs), architecture, engineering, and construction (AEC). Regarding decision-making and forecasting systems in particular, AI and machine learning (ML) technologies have provided an opportunity to improve the mutual relationships between machines and humans. When the connection between ML and architecture is considered, it is possible to claim that there is no parallel acceleration as in other disciplines. In this study, and considering the latest breakthroughs, we focus on revealing what ML and architecture have in common. Our focal point is to reveal common points by classifying and analyzing current literature through describing the potential of ML in architecture. Studies conducted using ML techniques and subsets of AI technologies were used in this paper, and the resulting data were interpreted using the bibliometric analysis method. In order to discuss the state-of-the-art research articles which have been published between 2014 and 2020, main subjects, subsets, and keywords were refined through the search engines. The statistical figures were demonstrated as huge datasets, and the results were clearly delineated through Sankey diagrams. Thanks to bibliometric analyses of the current literature of WOS (Web of Science), CUMINCAD (Cumulative Index about publications in Computer Aided Architectural Design supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD, and CAAD futures), predictable data have been presented allowing recommendations for possible future studies for researchers.
Digital technologies are viewing as a gateway to solve many of the problems arising from the crisis. How can we provide efficient education in COVID-19? To extend classroom learning to the home is a challenging task for the nations and the COVID-19 outbreak put the world in a situation from home learning. This study will analyze the overall resources and digital technologies available for education from home. For this purpose, Scopus and web of science databases are used to find the literature. The final 82 articles are select for the SLR (systematic literature review). The data inclusion and exclusion process are used in the PRISMA statement 2015 for quality assessment of data. The bibliometric analysis will conduct for text frequency co-occurrence and collaborations between the authors in countries. The vital step of data classification will process according to the author's segmentation and published research settings. In the last findings of the study, reports will explain past literature and recent outbreak. We are applying bibliometric analysis, showing that reviews are classified into 04 main clusters for analysis and discussion. These are 1) Digital Education Quick Shift Online Teaching and Learning for during Covid19 Pandemic 2) Digital Education during Pandemic and Rethinking for Sustainable Community; 3) Digital education for medical education and healthcare in hospital; 4) Digital Education and Digital innovation development during Covid19 pandemic. These findings are expected to benefit stakeholders studying and working relevant in digital education during and resilient post-Covid-19 pandemic.
Educational artificial intelligence (EAI) means an integration of artificial intelligence (AI) and educational science that is going to serve as an authoritative element of the education system in the future. However, limited bibliographic analysis study have been carried out with the purpose of conceptualising the advancements in this field of educational billiography This study aim is to identify and analysis the core topics using keywords. The method of study is using the Keywords and cluster analysis by conducting a bibliometric review of 8,660 articles that have been published from 2000 to 2020 with the help of CiteSpace software. The results reveal that EAI research primarily encompasses three controversial topics. There is controversy about the AI application to students, AI does not replace teachers and AI algorithms have great contribution in the development of education sector. Study concluded that, AI applications can improve the effectiveness of students’ learning, AI can replace part of the teachers’ work, the relationship between the teacher and the machine should be cooperation, not the relationship between replacement and being replaced, under the premise that teachers give full play to their initiative and innovation. Keywords: Educational artificial intelligence, core topics, Bibliometric analysis, CieSpace, Interactive learning environments
Artificial Intelligence (AI) came up as an ambiguous concept from computer sciences and now it is being used in many areas of our life. It has stimulated academia’s interest due to its alternative insights into complex problems. Therefore, a bibliometric method was applied in this study to observe the progress of AI in the tourism field. A total of 102 papers were collected from Scopus database. Key factors such as most productive authors, collaborations and institutions were identified, and research hotspots were determined using co-occurrence network and most common author keywords. Progress of AI was visualized with thematic evolution analysis. Findings indicate that there is a progressive interest in AI after 2017, and average citations signify that papers are highly cited. Since this is the first study conducting a bibliometric on AI in the tourism context, it could be considered useful for academics and tourism professionals as it provides general overview of AI, demonstrates research trends and popular papers.
Since the early 1990s, when digitalisation began to open new opportunities for disseminating information, many academic journals started to introduce online services. However, while some studies suggest that online availability and free access to journal articles are positively connected to the number of citations an article receives, little is known about whether being an early adopter of digital services provides journals with a (long-term) competitive advantage in times of digital change. We use data from SSCI-listed management journals to examine which journals pioneered the introduction of digital services, to what extent first-mover advantages can be identified, and which journal characteristics are associated with citation-based performance indicators. Our results show that lower ranked journals were the first to introduce digital services and were beneficiaries of the digital age. Furthermore, we find a negative correlation between general submission fees and journal performance and that the top-performing journals of our sample are those of non-commercial publishers. Our analysis of the relationship between journal performance and the provision of open access contradicts previous studies, as we find no positive correlations between performance and open access on the journal level.
The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasingly common in recent years, thanks in part to technological advancements and the heightened interest of the research and medical community. This study aims to provide a comprehensive picture of global trends and developments of AI applications relating to stroke and heart diseases, identifying research gaps and suggesting future directions for research and policy-making. A novel analysis approach that combined bibliometrics analysis with a more complex analysis of abstract content using exploratory factor analysis and Latent Dirichlet allocation, which uncovered emerging research domains and topics, was adopted. Data were extracted from the Web of Science database. Results showed topics with the most compelling growth to be AI for big data analysis, robotic prosthesis, robotics-assisted stroke rehabilitation, and minimally invasive surgery. The study also found an emerging landscape of research that was centered on population-specific and early detection of stroke and heart disease. Application of AI in health behavior tracking and improvement as well as the use of robotics in medical diagnostics and prognostication have also been found to attract significant research attention. In light of these findings, it is suggested that the currently under-researched issues of data management, AI model reliability, as well as validation of its clinical utility, need to be further explored in future research and policy decisions to maximize the benefits of AI applications in stroke and heart diseases.
Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.
Abstract
Integrating artificial intelligence (AI) stands out as the most dynamic and innovative breakthrough in introducing disruptive paths in the varied domains of education. This bibliometric analysis delved into the trajectory of AI’s evolving landscape within educational settings over two decades, encompassing 324 articles published from 2003 to 2023, sourced from the Scopus database. The study uncovers a substantial surge in publications with a steep increase from 2020, peaking in 2023. Notably, while established nations like China and the US lead in publications, notable contributions from other developing countries, including Saudi Arabia, India, and Malaysia, underscored a global shift. Key terms, including students, machine learning, AI and higher education, underpin the central focus research areas while emerging themes like “generative AI” and chatbots like “chatgpt” mark the evolving trends. Further, the study prompts sustained global partnerships, interdisciplinary collaborations, and continued exploration of emerging AI technologies to catalyze educational advancements.
Purpose
This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.
Design/methodology/approach
A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.
Findings
Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.
Originality/value
This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.
This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.
Bibliometric analysis is a popular and rigorous method for exploring and analyzing large volumes of scientific data. It enables us to unpack the evolutionary nuances of a specific field, while shedding light on the emerging areas in that field. Yet, its application in business research is relatively new, and in many instances, underdeveloped. Accordingly, we endeavor to present an overview of the bibliometric methodology, with a particular focus on its different techniques, while offering step-by-step guidelines that can be relied upon to rigorously perform bibliometric analysis with confidence. To this end, we also shed light on when and how bibliometric analysis should be used vis-à-vis other similar techniques such as meta-analysis and systematic literature reviews. As a whole, this paper should be a useful resource for gaining insights on the available techniques and procedures for carrying out studies using bibliometric analysis.
Keywords: Bibliometric analysis; Performance analysis; Science mapping; Citation analysis; Co-citation analysis; Bibliographic coupling; Co-word analysis; Network analysis; Guidelines.
Considering that modern science is conducted primarily through a network of collaborators who organize themselves around key researchers, this research develops and tests a characterization and assessment method that recognizes the particular endogenous, or self-organizing characteristics of research groups. Instead of establishing an ad-hoc unit of analysis and assuming an unspecified network structure, the proposed method uses knowledge footprints, based on backward citations, to measure and compare the performance/productivity of research groups. The method is demonstrated by ranking research groups in Physics, Applied Physics/Condensed Matter/Materials Science and Optics in the leading institutions in Mexico, the results show that the understanding of the scientific performance of an institution changes with a more careful account for the unit of analysis used in the assessment. Moreover, evaluations at the group level provide more accurate assessments since they allow for appropriate comparisons within subfields of science. The proposed method could be used to better understand the self-organizing mechanisms of research groups and have better assessment of their performance.