Agentic Artificial Intelligence Framework for Automated Adverse Drug Reaction Detection and Analysis

Adverse Drug Reactions (ADRs) remain one of the leading causes of patient morbidity, mortality, as well as growing healthcare cost across the world. The existing pharmacovigilance systems rely mostly on spontaneous reporting systems and manual and expert-based analytical processes, thus being limited in their scalability, timeliness, and breadth. The rapid growth of real-world data stores such as electronic health records, adverse event databases, and patient-generated drug reviews necessitates deployment of intelligent automation in order to develop an effective drug-safety monitoring. In this paper, we present a proposal of a framework by means of Agentic Artificial Intelligence (AI) that aims at supporting the automated identification, validation, and description of ADRs. The architecture is designed in terms of a modular, multi-agent system that includes Data Collection, ADR Extraction, Signal Detection, Validation, and Comparative Analysis agents. All the agents perform autonomous and specialized duties, but as a centralized body of ADR knowledge. The models of natural language processing are used to identify drug-ADR relationships in textual data that lacks structure, and statistical signal-detection methods are used to find nascent safety signals in structured databases, such as FAERS. The validation processes uses maintained repository of knowledge repositories, especially, SIDER, to increase reliability and reduce false-positive identifications. The framework allows the continuous monitoring, provides explainable outputs and it can be deployed at scale. According to the results of empirical studies, agentic AI is capable of significantly increasing the accuracy of ADR detection, minimizing the cost of operations, and enhancing patient safety. This study highlights the suitability of agentic AI systems as a new generation paradigm of intelligent pharmacovigilance.