Analysis of the hotspots and trends for artificial intelligence research of four major hospitals in the United States
10.3760/cma.j.cn111325-20241212-01049
- VernacularTitle:美国4家大型医院人工智能研究趋势及热点分析
- Author:
Zhiyuan HU
1
;
Yuanli LIU
1
Author Information
1. 中国医学科学院 北京协和医学院卫生健康管理政策学院，北京 100005
- Publication Type:Journal Article
- Keywords:
Artificial Intelligence;
Research hotspots;
Bibliometrics;
United States;
Hospitals;
Clinical translation and application
- From:
Chinese Journal of Hospital Administration
2025;41(8):624-629
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the research trends and hotspots of artificial intelligence (AI) in four top-tier large hospitals in the United States based on bibliometrics, for references for hospitals in China.Methods:From the Web of Science Core Collections database, AI research papers published by researchers affiliated with four major hospitals in the United States (Mayo Clinic, Cleveland Clinic, The Johns Hopkins Hospital, and Massachusetts General Hospital) from 1991 to 2023 were retrieved. Bibliometric analysis, co-occurrence network analysis and thematic analysis were adopted to analyze the annual number of published papers, number of literature citations, high-frequency author collaboration networks, high-frequency keywords, and research hotspots.Results:A total of 509 English-language literatures were included, and the number of published papers showed explosive growth starting from 2021, with the highest number of publications (153 papers) in 2023. Massachusetts General Hospital had the largest number of papers, totaling 191. 10 papers had more than 240 citations. From the perspective of high-frequency author collaboration networks, two large research teams and five small research teams have been formed, and cross team collaboration has emerged. 45 high-frequency keywords (frequency≥4 times) shaped three clusters, inluding artificial intelligence technology, cardiovascular disease, and medical imaging. The research hotspots had shifted from traditional technology development to multi scenario applications driven by machine learning and deep learning.Conclusions:In recent years, AI related research in four large hospitals in the United States had been in a period of rapid growth, with the number of papers increasing year by year. Mature research teams had been formed, and research trends were rapidly in line with advances in AI technology. Chinese hospitals could learn from their developmental experiences, increase cross institutional cooperation research efforts, strengthen the clinical translation and application of medical AI technology, and continuously promote the high-quality development of medical AI research in China.
