Artificial intelligence facilitates tropical infectious disease control and research
10.16250/j.32.1374.2022167
- VernacularTitle:人工智能助力热带传染病防控研究
- Author:
Liang SHI
1
;
Jian-feng ZHANG
1
;
Wei LI
1
;
Kun YANG
2
Author Information
1. Jiangsu Institute of Parasitic Diseases, National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Wuxi, Jiangsu 214064, China; Public Health Research Center, Jiangnan University, Wuxi, Jiangsu 214064, China
2. Jiangsu Institute of Parasitic Diseases, National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Wuxi, Jiangsu 214064, China; Public Health Research Center, Jiangnan University, Wuxi, Jiangsu 214064, China; School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Publication Type:Journal Article
- Keywords:
Tropical infectious disease;
Artificial intelligence;
Machine learning;
Deep learning;
Public health;
Global health
- From:
Chinese Journal of Schistosomiasis Control
2022;34(5):445-452
- CountryChina
- Language:Chinese
-
Abstract:
Since the global pandemic of coronavirus disease 2019 (COVID-19) in late 2019, artificial intelligence technology has shown increasing values in the research and control of tropical infectious diseases. The introduction of artificial intelligence technology has shown remarkable effectiveness to reduce the diagnosis and treatment burdens, reduce missing diagnosis and misdiagnosis, improve the surveillance and forecast ability and enhance the medicine and vaccine development efficiency. This paper summarizes the current applications of artificial intelligence in tropical infectious disease control and research and discusses the important values of artificial intelligence in disease diagnosis and treatment, disease surveillance and forecast, vaccine and drug discovery, medical and public health services and global health governance. However, artificial intelligence technology suffers from problems of single and inaccurate diagnosis, poor disease surveillance and forecast ability in open environments, limited capability of intelligent system services, big data management and model interpretability. Hereby, we propose suggestions with aims to improve multimodal intelligent diagnosis of multiple tropical infectious diseases, emphasize intelligent surveillance and forecast of vectors and high-risk populations in open environments, accelerate the research and development of intelligent management system, strengthen ethical security, big data management and model interpretability.
