AI-Driven Predictive Analytics For Medicinal Product Recall Management: Enhancing Traceability And Regulatory Compliance

Abstract

The pharmaceutical industry faces increasing challenges in managing its products, ensuring patient safety, and maintaining regulatory compliance. Traditional recall mechanisms depend on reactive approaches that often result in broad product withdrawal and an extended response deadline. Integration of artificial intelligence with serialization infrastructure represents a transformative opportunity to increase pharmaceutical recall management through future analytics and accurate targeting. The verification router service architecture provides a fundamental outline for real-time product authentication in the complex supply chain network. Machine Learning algorithms demonstrate an extraordinary ability to identify initial warning indicators within the serialization dataset, enabling active intervention strategies that prevent potential recall scenarios. Implementation challenges include data quality requirements, regulatory verification protocols, and adequate capital investment. The convergence of an AI-powered future analysis with the pharmaceutical serialization system provides significant benefits, including low recall cost, increased patient safety results, and supply chain transparency.