With the increasing maturity of wearable technologies, the value of wearable devices in health monitoring, health assessment and health intervention has been revealed gradually, which will help promote the innovation and development of health management.
To analyze the research hotspots, frontiers, and trends of wearable devices in health management in China in recent 10 years.
The periodical literature with the theme of "wearable" included in the CNKI database from 2011 to 2021 was retrieved and analyzed. Excel was utilized to analyze the temporal and spatial distribution of the included literature. The keywords were visually analyzed by CiteSpace.
The number of published papers on wearable devices in health management in China showed an overall upward trend from 2011 to 2021 (n=519) , with the maximum quantity in 2021 (n=85) . Related researches involved multiple disciplines including biomedicine, information science, computer hardware and software technology, and published in various journals such as the Journal of Medical Informatics, China Digital Medicine, and Smart Healthcare. The top three prolific researchers included Professor ZHANG Zhengbo from the Chinese PLA General Hospital, Associate professor LUO Xiaolan from Shanghai University of Traditional Chinese Medicine, and Professor HE Xiaolin from Institute of Medical Information, Chinese Academy of Medical Sciences. The top three prolific research institutions were Huazhong University of Science and Technology (14 papers) , Shanghai Jiaotong University (10 papers) and Southeast University (10 papers) . The keywords reflecting research hotspots included "mobile medicine" , "health management" and "smart medicine" , and those reflecting research frontiers were "elderly people" , "diabetes" and "arrhythmia" . The keywords related to research trends included "5G" , "monitoring system" and "chronic disease" .
The research on wearable devices in health management in China during 2011 to 2021 focused on the health monitoring for the elderly and the management of chronic diseases such as diabetes. The frontiers of the research were the early warning and prediction of abnormalities in heart rate, blood pressure and blood glucose, etc. And the research trend was constructing a complete health management system that is involved in health monitoring, health risk assessment, health intervention and promotion.
The past nearly 20-year period has seen a sudden increase in the use of artificial intelligence (AI) in esophageal cancer research, and an emergence of many systematic reviews and meta-analyses of the research. However, most of the reviews and meta-analyses only address a single aspect in summary, making it difficult for researchers to gain a comprehensive understanding of the latest developments and research hotspots in the field.
To perform a bibliometric analysis of the use of AI in esophageal cancer research, and the development, hotspots and emerging trend in this field.
All literature in English regarding esophageal cancer research using AI included in the Science Citation Index Expanded database of the Web of Science Core Collection was searched from 2000-01-01 to 2022-04-06. Microsoft Excel 2019, CiteSpace (5.8R3-64bit) and VOSviewer (1.6.18) were used to analyze the literature for annual number of publications, country, author, institution, co-citation and keywords.
Nine hundred and eighteen studies were retrieved, with a total of 23 490 times of being cited. The number of studies published between 2000 and 2016 grew slowly (from 6 to 40), but increased rapidly between 2017 and 2022 (from 62 to 216). Sixty countries, 118 institutions and 5 979 authors were involved in the studies. China (306 articles), the United States (238 articles) and the United Kingdom (113 articles) ranked the top three in terms of number of studies published. The top three institutions in terms of intensity of cooperation were University of Amsterdam (TLS=72), Catherine Hospital (TLS=64) and Eindhoven University of Technology (TLS=53). The top three authors in terms of number of publications were Jacques J G H M Bergman from the Netherlands (n=16), Tomohiro Tada from Japan (n=12), and Fons Van Der Sommen from the Netherlands (n=12). There were 39 962 co-cited authors and 42 992 co-cited studies. Thirty-three burst keywords were identified: the major burst keywords were p53 and mutations in 2001-2008 (early stage), and were esophageal cancer classification, new examination techniques (tomography), differentiation, identification and comparison between esophageal cancer and other cancers in 2013-2018 (middle stage), and were deep learning, convolutional neural network, and machine learning in esophageal cancer examination and diagnosis applications in 2019-2022 (late stage). Among which deep learning had the highest burst intensity (burst intensity of 13.89) .
AI application in esophageal cancer research has entered a new phase, moving gradually from genes and mutations toward accurate examination, diagnosis, and treatment. The latest major burst keywords in recent years (2019-2022) are deep learning, convolutional neural network, and machine learning in esophageal cancer examination and diagnosis. The future challenges to the use of AI in esophageal cancer research may include individual data collection, data quality assurance, data processing specifications, AI code reproduction, and reliability assurance of AI-assisted diagnostic decision-making.
