Underlying architecture characteristics of the U.S. FDA AEMS and risk management

doi:10.19803/j.1672-8629.20260388

Abstract

Abstract: Objective To explore the underlying architecture of the U.S. FDA's new unified Adverse Event Management System (AEMS) and to analyze its approaches to data governance and risk management so as to offer insights for China's AI+drug regulation framework and improve post-marketing risk perception capabilities. Methods We traced the FDA's evolution from a collection of fragmented, standalone databases to the integrated AEMS platform and identified both the strengths and weaknesses of its design in terms of cross-modal data integration, ontology mapping, and the use of AI algorithms. Results Our analysis found that the cloud-native AEMS eliminated information silos, supported dynamic data updates through APIs, and used natural language processing (NLP) models to streamline data preprocessing. These capabilities could improve signal detection and shorten early-warning cycles. However, the system's high level of transparency could give rise to a transparency paradox, which might potentially trigger unnecessary public alarm resulting from exposing industry to the dual pressures of compliance burdens and information overload. Conclusion It is recommended that China develop a unified pharma-covigilance database covering drugs, cosmetics, and medical devices while strengthening interdisciplinary collaboration and human-machine collaborative tiered review to encourage domestic enterprises to build forward-looking, proactive risk management systems.

Key words: FDA, AEMS, Pharmacovigilance, Risk, Under-lying Architecture

CLC Number:

R951
R994.11

Jin Kaili, Chen Xi, Xiong Jiamin, Ye Xiaofei, Guo Xiaojing. Underlying architecture characteristics of the U.S. FDA AEMS and risk management[J]. Chinese Journal of Pharmacovigilance, 2026, 23(9): 961-967.

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URL: https://zgywjj.magtechjournal.com/EN/10.19803/j.1672-8629.20260388

https://zgywjj.magtechjournal.com/EN/Y2026/V23/I9/961

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