Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources

Likeng Liang, Jifa Hu, Gang Sun, Na Hong, Ge Wu, Yuejun He, Yong Li, Tianyong Hao, Li Liu and Mengchun Gong ()
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Likeng Liang: South China Normal University
Jifa Hu: Huazhong University of Science and Technology
Gang Sun: The Affiliated Cancer Hospital of Xinjiang Medical University
Na Hong: Digital Health China Technologies Co., Ltd.
Ge Wu: Digital Health China Technologies Co., Ltd.
Yuejun He: Digital Health China Technologies Co., Ltd.
Yong Li: South China Normal University
Tianyong Hao: South China Normal University
Li Liu: Southern Medical University
Mengchun Gong: Southern Medical University

Drug Safety, 2022, vol. 45, issue 5, No 10, 519 pages

Abstract: 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.

Date: 2022
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DOI: 10.1007/s40264-022-01170-7

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