[Analysis of the global competitive landscape in artificial intelligence medical device research].

Journal: Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Published Date:

Abstract

The objective of this study is to map the global scientific competitive landscape in the field of artificial intelligence (AI) medical devices using scientific data. A bibliometric analysis was conducted using the Web of Science Core Collection to examine global research trends in AI-based medical devices. As of the end of 2023, a total of 55 147 relevant publications were identified worldwide, with 76.6% published between 2018 and 2024. Research in this field has primarily focused on AI-assisted medical image and physiological signal analysis. At the national level, China (17 991 publications) and the United States (14 032 publications) lead in output. China has shown a rapid increase in publication volume, with its 2023 output exceeding twice that of the U.S.; however, the U.S. maintains a higher average citation per paper (China: 16.29; U.S.: 35.99). At the institutional level, seven Chinese institutions and three U.S. institutions rank among the global top ten in terms of publication volume. At the researcher level, prominent contributors include Acharya U Rajendra, Rueckert Daniel and Tian Jie, who have extensively explored AI-assisted medical imaging. Some researchers have specialized in specific imaging applications, such as Yang Xiaofeng (AI-assisted precision radiotherapy for tumors) and Shen Dinggang (brain imaging analysis). Others, including Gao Xiaorong and Ming Dong, focus on AI-assisted physiological signal analysis. The results confirm the rapid global development of AI in the medical device field, with "AI + imaging" emerging as the most mature direction. China and the U.S. maintain absolute leadership in this area-China slightly leads in publication volume, while the U.S., having started earlier, demonstrates higher research quality. Both countries host a large number of active research teams in this domain.

Authors

  • Juan Chen
    Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China. chenjuan94@bjmu.edu.cn.
  • Lizi Pan
    Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100020, P. R. China.
  • Junyu Long
    School of Medical Humanity, Peking University Health Science Center, Beijing 100191, P. R. China.
  • Nan Yang
    Department of Infectious Disease, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China. Electronic address: Shmily.1989.2008@stu.xjtu.edu.cn.
  • Fei Liu
    Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
  • Yan Lu
    National Institute of Standards and Technology.
  • Zhaolian Ouyang
    Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.