药物警戒
医学
资源(消歧)
政府(语言学)
知识管理
数据科学
质量(理念)
计算机科学
药理学
不利影响
计算机网络
语言学
认识论
哲学
作者
Likeng Liang,Jifa Hu,Gang Sun,Na Hong,Ge Wu,Yuejun He,Yong Li,Tianyong Hao,Li Liu,Mengchun Gong
出处
期刊:Drug Safety
[Adis, Springer Healthcare]
日期:2022-05-01
卷期号:45 (5): 511-519
被引量:39
标识
DOI:10.1007/s40264-022-01170-7
摘要
With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner, data-driven automatic methods need to be urgently applied to all aspects of pharmacovigilance to assist healthcare professionals. However, the quantity and quality of data directly affect the performance of AI, and there are particular challenges to implementing AI in limited-resource settings. Analyzing challenges and solutions for AI-based pharmacovigilance in resource-limited settings can improve pharmacovigilance frameworks and capabilities in these settings. In this review, we summarize the challenges into four categories: establishing a database for an AI-based pharmacovigilance system, lack of human resources, weak AI technology and insufficient government support. This study also discusses possible solutions and future perspectives on AI-based pharmacovigilance in resource-limited settings.
科研通智能强力驱动
Strongly Powered by AbleSci AI