Artificial Intelligence in Pharmaceutical Quality Assurance: Applications, Challenges and Future Perspective

The pharmaceutical and healthcare industries have benefited significantly from advances in artificial intelligence in recent years. In the pharmaceutical sector, artificial intelligence (AI) has become a game-changing technology, especially in Quality Assurance (QA). Through advanced data analytics, machine learning (ML), deep learning (DL), natural language processing (NLP), and predictive modelling, AI-driven solutions enhance production productivity, product quality, automation, accuracy, regulatory compliance, and decision-making. Real-time monitoring, automated documentation, anomaly detection, predictive maintenance, and risk-based quality management are all made possible through the use of AI into pharmaceutical QA. Despite its benefits, implementation is still severely hampered by obstacles including data integrity, regulatory ambiguity, model validation, cybersecurity, and ethical dilemmas. These technologies lower human error and enhance product quality while supporting continuous manufacturing, Process Analytical Technology (PAT), and Quality by Design (QbD). This review discusses the applications, benefits, challenges, limitations, and future prospectives of AI in pharmaceutical Quality Assurance.