Pharmacovigilance, the process of monitoring and evaluating adverse drug reactions, is a critical component of healthcare systems. However, in resource-limited settings, the effective implementation of pharmacovigilance faces numerous challenges, including insufficient infrastructure, limited access to healthcare data, and a shortage of trained personnel. The advent of artificial intelligence (AI) offers promising solutions to these challenges by automating data collection, enhancing signal detection, and improving the overall efficiency of pharmacovigilance activities. This review explores the potential of AI-driven pharmacovigilance in resource-limited settings, examining current applications, potential benefits, and the challenges that need to be addressed for successful implementation. We highlight case studies where AI has been successfully integrated into pharmacovigilance processes and discuss future directions for research and development in this emerging field. Our findings suggest that while AI has the potential to revolutionize pharmacovigilance in resource-limited settings, careful consideration of ethical, technical, and contextual factors is essential for its sustainable and equitable deployment.
Artificial Intelligence, Pharmacovigilance, Resource-Limited Settings, Adverse Drug Reactions, Healthcare Automation, Signal Detection, Healthcare Data, AI in Healthcare, Public Health Monitoring, Ethical Considerations in AI
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