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.
Artificial Intelligence ; Bibliometrics ; Humans ; China ; Equipment and Supplies ; United States ; Biomedical Research

Objective To explore the technology frontiers for neuroblastoma treatment from the perspective of patent citation network. Methods Through patent analysis for neuroblastoma treatment, highly cited patents and highly cited papers in the citation network were taken as the research objects. The title and abstract of the citing patents were analyzed by text clustering to identify the technology frontiers. Through social network analysis, the core patents were identified from the indices of degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Results A total of 6240 patent applications for neuroblastoma treatment were found, including 71304 patent citations and 88698 journal-article citations. Four technology frontiers were identified based on patent citation network, namely, drug target, drug design, tumor-indication expansion, and gene-expression regulation. Three technology frontiers were identified based on journal-article citation network. They were drug target, drug design, and tumor-indication expansion. Conclusion The development of technology for neuroblastoma treatment continues to be active. Drug target and drug design are the most important technology frontiers. This study could provide certain reference for neuroblastoma treatment from the perspective of information science.

Objective To study long term renal function of Donation after citizen's deceased transplantation.Methods We compared the data of 38 subjects who got Delayed Graft Function(DGF) with 80 Immediate Graft Function (IGF) subjects underwent DCD transplantation in our hospital before June 2016.Evaluated the renal function by detecting the serum creatinine (sCr),the estimating glomerular filtration rate (eGFR) calculated with MDRD formula and urine protein at the 1,2,3 year post transplantation.Results Analyzed the serum eGFR of two groups,there was no significant differences at 1 and 2 year post transplantation,sCr of two groups showed no significant differences at 3 year (P =0.053)post transplantation,eGFR of two groups showed significant differences at 3 year (P =0.042)post transplantation and positive incidence of urine protein showed significant differences at 2 year (P =0.028)and 3 year (P =0.037)post transplantation.Conclusion DGFoccuring after DCD transplantation had an effect on long term renal function,.mainly on reducing of eGFR and increasing of urine protein positive rate 2 or 3 years after transplant.