INTEGRATING ARTIFICIAL INTELLIGENCE IN QUALITY AUDITS OF ORAL SOLID DOSAGE MANUFACTURING FACILITIES
DOI:
https://doi.org/10.22159/ijap.2026v18i2.57414Keywords:
Artificial intelligence (AI) audits, GMP compliance, Natural language processing (NLP), Rule-based scoring, WinAI, Audit automation, Regulatory technologyAbstract
Objective: Traditional good manufacturing practice (GMP) audits in pharmaceutical manufacturing are time-consuming, labour-intensive, and subject to inspector bias, leading to inconsistent severity grading, recurring non-compliance, and limited cross-facility trend analysis. This study aimed to refine and preliminarily validate WinAI, an artificial intelligence (AI)–enabled, web-based audit platform for oral solid dosage (OSD) manufacturing, designed to harmonize GMP audits, reduce subjectivity, and enable predictive and data-driven oversight while maintaining good practice (GxP) principles.Methods: WinAI integrates harmonized GMP checklists, natural language processing (NLP), a deterministic rule-based severity scoring engine, and a historical database for recurrence detection. The system architecture includes a front-end audit user interface, a back-end rule engine, an NLP pipeline with interpreters, and a recurrence-detection module. Severity classification (critical, major, minor) is based on predefined rule sets with weighted aggregation across six GMP systems. Pilot validation was conducted using 254 simulated and 12 limited live OSD audit datasets, with expert auditors serving as the reference standard. Performance metrics included classification accuracy, audit duration, and inter-rater consistency.
Results: Preliminary pilot testing demonstrated that WinAI achieved high accuracy in classifying GMP deviations, with an overall accuracy of 95.3% and a macro-F1 score of 94.1%. The platform reduced audit duration by approximately 40% compared to conventional audit practices. The recurrence-detection logic successfully identified repeated non-conformances and automatically escalated severity for recurring issues, thereby supporting more effective corrective and preventive action (CAPA) management.
Conclusion: WinAI provides an auditable, harmonized, and data-driven approach to GMP auditing that reduces inspector bias, shortens audit timelines, and increases focus on systemic and recurrent non-conformances. The platform is designed to meet GxP validation expectations and supports phased integration with regulatory inspection databases, offering a scalable solution for enhanced regulatory oversight in pharmaceutical manufacturing.
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