Topics and trends in artificial intelligence assisted human brain research

Chen, Xieling; Chen, Juan; Cheng, Gary; Gong, Tao

doi:10.1371/journal.pone.0231192

Cited by 42 publications

(22 citation statements)

References 113 publications

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“…For quality assurance, the data retrieval was conducted in SCIE and SSCI databases, following previous review studies (e.g., [ 17 , 18 ]), because of the wide recognition of them [ 19 , 20 ]. At the prior stage of this study, we included book chapters, conference papers, non-SCIE and non-SSCI journal articles in the databases; after having read a few, however, we found problems of these papers and decided to exclude them.…”

Section: Methodsmentioning

confidence: 99%

Game-based self-regulated language learning: Theoretical analysis and bibliometrics

2020

Self Cite

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Game-based learning and self-regulated learning have long been valued as effective approaches to language education. However, little research has been conducted to investigate their integration, namely, game-based self-regulated language learning (GBSRLL). This study aims to conceptualise GBSRLL based on the combination of theoretical analysis, thematic evolution analysis, and social network analysis on the research articles in the fields of game-based language learning and self-regulated language learning. The results show that GBSRLL is a new interdisciplinary field emerging since the period from 2018 to 2019. Self-regulated learning strategies that can be performed in GBSRLL, the effects of GBSRLL on learners’ affective states, and the features in GBSRLL were the prominent research topics in this field. Its theoretical foundation centres on the positive correlations between learner motivation, self-efficacy, and autonomy and the implementation of game-based learning and self-regulated learning. It is feasible to conduct GBSRLL due to the strong supportiveness of game mechanics for various phases and strategies of self-regulated learning. More contributions to this new interdisciplinary field are called for, especially from the aspects of the long-term effects of GBSRLL on academic performance and the useful tools and technologies for implementing GBSRLL.

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Section: Methodsmentioning

confidence: 99%

Game-based self-regulated language learning: Theoretical analysis and bibliometrics

2020

Self Cite

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“…Bibliometric analysis is a method utilized in many fields to illustrate the landscape of subjects [12,13]. Based on CT, bibliometric analysis objectively includes articles of subject on subject in the analysis and reflects the evolution of a subject, trends of popular topics and collaborative relationships among researchers and countries [14][15][16]. To determine the influential research, the distribution of disparate topics and the evolution of research trends, we performed a bibliometric analysis of the top 100 most cited articles in clinical and experimental studies of DKD.…”

Section: Introductionmentioning

confidence: 99%

The evolution and future of diabetic kidney disease research: a bibliometric analysis

Wei

Jiang

2021

BMC Nephrol

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Background Diabetic kidney disease (DKD) is one of the most important complications of diabetic mellitus. It is essential for nephrologists to understand the evolution and development trends of DKD. Methods Based on the total cited numbers in the Web of Science Core Collection, which was searched through September 28th, 2020, we performed a bibliometric analysis of the top 100 most cited full-length original articles on the subject of DKD. The timespans, authors, contributions, subcategories, and topics of those 100 articles were analysed. In addition, the evolution of topics in DKD research was investigated. Results There were 23,968 items under the subject of DKD in the Web of Science Core Collection. The top 100 cited articles, published from 1999 to 2017, were cited 38,855 times in total. Researchers from the USA contributed the most publications. The number of articles included in ‘Experimental studies (EG)’, ‘Clinical studies (CS)’, ‘Epidemiological studies (ES)’, and ‘Pathological and pathophysiological studies (PP)’ were 65, 26, 7, and 2, respectively. Among the 15 topics, the most popular topic is the renin-angiotensin-aldosterone system (RAAS), occurring in 26 articles, including 6 of the top 10 most cited articles. The evolution of topics reveals that the role of RAAS inhibitor is a continuous hotspot, and sodium-glucose cotransporter 2 (SGLT-2) inhibitor and glucagon-like peptide 1 (GLP-1) agonist are two renoprotective agents which represent novel therapeutic methods in DKD. In addition, the 26 clinical studies among the top 100 most cited articles were highlighted, as they help guide clinical practice to better serve patients. Conclusions This bibliometric analysis of the top 100 most cited articles revealed important studies, popular topics, and trends in DKD research to assist researchers in further understanding the subject.

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“…Such assessments are critical for prioritizing research and mobilizing resources for addressing research gaps in different branches of knowledge. Several bibliometric studies have shown the evolution of AI applications in different fields of health sciences [9,16,17]. Moreover, emerging bibliometric studies have reported how COVID-19 related research is increasingly produced globally [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence Technologies in COVID-19 Research: A Bibliometric Study

Mm¹,

Sa²,

Elj³

et al. 2020

Preprint

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The novel coronavirus disease (COVID-19) pandemic has impacted health and wellbeing globally. To strengthen preventive and clinical care amid this pandemic, technological innovations like artificial intelligence (AI) are increasingly used in different contexts. This bibliometric study aimed to assess the current scholarly development and prominent research domains in applications of AI technologies in COVID-19 research. A total of 105 articles were retrieved from MEDLINE database that emphasized on the use of AI in the context of COVID-19. Most articles had multiple authors with a collaboration index of 7.18. Moreover, most of the articles were produced from the USA (22.86%) and China (21.9%), whereas developing countries were underrepresented among the contributing nations. Furthermore, several research domains were identified, including prevention and control, diagnostics, epidemiological characteristics, therapeutics, psychological conditions, and different areas of data sciences related to COVID-19. The current bibliometric evidence shows the early stage of development in this field, which necessitates equitable applications of AI in COVID-19 research emphasizing on health disparities, socio-legal issues, vaccine development, and applied public health research in this pandemic.

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Topics and trends in artificial intelligence assisted human brain research

Cited by 42 publications

References 113 publications

Game-based self-regulated language learning: Theoretical analysis and bibliometrics

Game-based self-regulated language learning: Theoretical analysis and bibliometrics

The evolution and future of diabetic kidney disease research: a bibliometric analysis

Applications of Artificial Intelligence Technologies in COVID-19 Research: A Bibliometric Study

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