(M1030-05-33) AI-Enhanced Regulatory Inspection Platform: A Multi-Modal System for Automated Report Generation and Compliance Assessment

Purpose: To develop and validate an AI-enhanced regulatory inspection platform that integrates artificial intelligence, machine learning, and natural language processing technologies to improve the efficiency, accuracy, and standardization of regulatory inspection reporting processes, with initial focus on Good Manufacturing Practice (GMP) compliance in pharmaceutical facilities.

Methods: The system integrates five specialized modules: (1) a statement refinement module using Large Language Models (32B parameter models) for real-time grammar correction and terminology normalization, (2) a guideline indexing module employing ColBERT-based semantic search for GMP guideline vector embeddings, (3) a guideline search module for contextual clause mapping, (4) a classification module using machine learning algorithms (linear regression, support vector machines) for multi-level criticality assessment, and (5) a report generation module for standardized output. The platform was implemented as a Proof of Concept for GMP inspection reporting in Saudi Arabian pharmaceutical facilities and tested through a three-phase implementation roadmap in collaboration with Saudi Food & Drug Authority.

Results: Performance evaluation demonstrated dramatic time savings with report generation reduced from 7-21 days to less than 2 hours (84-252x improvement), while achieving high accuracy rates in statement refinement (85-90%), clause mapping (80%+), and criticality assessment (76%). The AI enhanced platform reduced note-taking and statement refinement time, enhanced report quality and consistency, and received positive user feedback from participating inspectors, while demonstrating superior performance compared to traditional manual reporting methods.

Conclusion: The AI-Enhanced Regulatory Inspection Platform successfully transforms regulatory inspection reporting by delivering unprecedented efficiency gains and quality improvements. The platform would enable a 5-7x increase in inspection frequency, substantially expanding regulatory oversight capacity and enhancing product safety surveillance. Advanced analytics capabilities provide predictive insights for proactive compliance management, early risk detection, and evidence-based regulatory decision-making. Beyond individual performance improvements, the system establishes a foundation for industry-wide transformation through standardized reporting, reduced inter-inspector variability, and continuous learning mechanisms. The modular architecture ensures extensibility across diverse regulatory domains, positioning this technology as a paradigm shift toward intelligent, data-driven regulatory compliance that enhances public health protection while optimizing resource utilization and operational efficiency.

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Performance metrics comparison: Traditional vs. AI-assisted regulatory inspection systems

Comparison of the improved accuracy across the three tested AI modules between 1st and 2nd testing cycles showing model improvement after training

End-to-end inspection reporting workflow