Artificial intelligence in epidemiology: a decade-long bibliometric analysis
10.3760/cma.j.cn112338-20250118-00049
- VernacularTitle:人工智能在流行病学中的应用：十年文献计量学分析
- Author:
Conghui WANG
1
;
Ziming YANG
;
Wei SHI
;
Chengwei XI
;
Shucheng SI
;
Liuliu WU
;
Jian DU
;
Shengfeng WANG
;
Siyan ZHAN
Author Information
1. 内蒙古自治区药物警戒中心药品监测与评价科，呼和浩特 010010
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Epidemiology;
Bibliometrics;
Research hotspots;
Advantages and challenges
- From:
Chinese Journal of Epidemiology
2025;46(9):1650-1659
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.
