Main Article Content
Abstract
Background: Pharmaceutical manufacturing operates under stringent Good Manufacturing Practice (GMP) and data integrity standards, where quality control (QC) remains central to product safety and regulatory compliance. Traditional QC processes rely heavily on manual inspection, documentation, and operator-dependent decision-making, all of which introduce variability, delays, and vulnerability to human error. Artificial intelligence (AI) has emerged as a transformative technology capable of strengthening accuracy, consistency, and efficiency in pharmaceutical quality control systems.
Aim: This research examines how AI enhances quality control in pharmaceutical manufacturing, focusing on its applications in defect detection, real-time monitoring, predictive analytics, and documentation integrity. The study evaluates practitioner insights on AI’s operational, organisational, and regulatory impacts using qualitative evidence from pharmaceutical professionals.
Methods: A qualitative, exploratory research design was adopted, following an interpretivist philosophy. Semi-structured interviews, as documented were conducted with professionals involved in QC, QA, manufacturing, and digital transformation roles. Thematic analysis was used to identify patterns relating to AI adoption, QC performance improvement, data integrity, organisational readiness, and regulatory expectations. Secondary scientific literature supported triangulation.
Results: Five major AI-related QC themes emerged:
(1) AI-driven visual inspection and defect detection improved accuracy, sensitivity, and repeatability;
(2) Predictive analytics enhanced early deviation detection and prevented equipment-related failures;
(3) AI-enabled real-time process monitoring increased batch reliability and reduced OOS events;
(4) AI-supported digital documentation and data integrity tools reduced errors and strengthened compliance;
(5) Barriers to AI adoption, including skill gaps, trust issues, legacy infrastructure, and regulatory uncertainty, limited scaling. Collectively, AI substantially improved QC precision, operational consistency, and regulatory alignment.
Conclusion: AI significantly enhances pharmaceutical quality control by enabling predictive, accurate, data-driven decisions that minimize human error and strengthen GMP compliance. However, successful implementation requires robust digital infrastructure, clear regulatory validation strategies, workforce upskilling, and cross-functional organisational support. AI represents a critical enabler of next-generation QC, offering pharmaceutical manufacturers improved product assurance, operational resilience, and long-term competitiveness.
Aim: This research examines how AI enhances quality control in pharmaceutical manufacturing, focusing on its applications in defect detection, real-time monitoring, predictive analytics, and documentation integrity. The study evaluates practitioner insights on AI’s operational, organisational, and regulatory impacts using qualitative evidence from pharmaceutical professionals.
Methods: A qualitative, exploratory research design was adopted, following an interpretivist philosophy. Semi-structured interviews, as documented were conducted with professionals involved in QC, QA, manufacturing, and digital transformation roles. Thematic analysis was used to identify patterns relating to AI adoption, QC performance improvement, data integrity, organisational readiness, and regulatory expectations. Secondary scientific literature supported triangulation.
Results: Five major AI-related QC themes emerged:
(1) AI-driven visual inspection and defect detection improved accuracy, sensitivity, and repeatability;
(2) Predictive analytics enhanced early deviation detection and prevented equipment-related failures;
(3) AI-enabled real-time process monitoring increased batch reliability and reduced OOS events;
(4) AI-supported digital documentation and data integrity tools reduced errors and strengthened compliance;
(5) Barriers to AI adoption, including skill gaps, trust issues, legacy infrastructure, and regulatory uncertainty, limited scaling. Collectively, AI substantially improved QC precision, operational consistency, and regulatory alignment.
Conclusion: AI significantly enhances pharmaceutical quality control by enabling predictive, accurate, data-driven decisions that minimize human error and strengthen GMP compliance. However, successful implementation requires robust digital infrastructure, clear regulatory validation strategies, workforce upskilling, and cross-functional organisational support. AI represents a critical enabler of next-generation QC, offering pharmaceutical manufacturers improved product assurance, operational resilience, and long-term competitiveness.
Keywords
Artificial intelligence; pharmaceutical quality control; defect detection; predictive analytics; digital transformation; GMP compliance; data integrity; pharmaceutical manufacturing.
Article Details
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