Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources.

Journal: Drug safety
Published Date:

Abstract

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.

Authors

  • Likeng Liang
    School of Computer Science, South China Normal University, Guangzhou, China.
  • Jifa Hu
    The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Gang Sun
    Key Laboratory of Oncology of Xinjiang Uyghur Autonomous Region, The Affiliated Cancer Hospital of Xinjiang Medical University, Ürümqi, China.
  • Na Hong
    Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, United States.
  • Ge Wu
    Ping An Technology (Shenzhen) Co., Ltd., Shanghai, China.
  • Yuejun He
    Digital Health China Technologies Co., Ltd., Beijing, China.
  • Yong Li
    Department of Surgical Sciences, Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI, United States.
  • Tianyong Hao
    School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China. haoty@gdufs.edu.cn.
  • Li Liu
    Metanotitia Inc., Shenzhen, China.
  • Mengchun Gong
    Institute of Health Management, Southern Medical University, No. 1023-1063, Shatai South Road, Guangzhou 510515, People's Republic of China.