STANDARDIZATION AND DATA INTEGRATION CHALLENGES IN AI-ENABLED PHARMACEUTICAL QUALITY ASSURANCE SYSTEMS: REGULATORY BARRIERS AND FUTURE DIRECTIONS

The Artificial Intelligence (AI) technology is currently revolutionizing pharmaceutical quality assurance (QA) systems through its ability to enhance process efficiency and through its development of predictive analytics and deviation management and batch release decision making and real-time quality monitoring capabilities. The AI-powered QA systems of pharmaceutical companies use machine learning and computer vision and natural language processing and advanced analytics to develop their manufacturing and compliance processes. The process of implementing large-scale operations faces significant obstacles which arise from the need to achieve standardized data and system interoperability while dealing with multiple components of digital infrastructure and inconsistent data documentation and changing regulatory requirements. Pharmaceutical organizations face challenges in their operations because they use various traditional systems which include laboratory information management systems (LIMS) and manufacturing execution systems (MES) and enterprise resource planning (ERP) and quality management systems (QMS). AI-driven systems must meet regulatory agency requirements which include demonstrating transparency and achieving validation and auditability and ensuring data integrity and cybersecurity and establishing continuous performance monitoring. The research review evaluates different standardization and data integration issues which hinder artificial intelligence implementation in pharmaceutical quality assurance systems. The study examines various regulatory obstacles which international organizations such as the US FDA and EMA and ICH and WHO and PIC/S have established. The research presents upcoming technology solutions which include cloud-based unified platforms and FAIR data principles and blockchain traceability and federated learning and explainable AI and risk-based governance models. The research paper demonstrates future pathways which need to be followed in order to create strong compliance systems.