Harnessing AI And Machine Learning in Pharmaceutical Quality Assurance

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical quality assurance (QA) presents transformative opportunities for improving the accuracy, efficiency, and consistency of quality control processes. This review explores the key applications of AI/ML in QA, including data analysis, predictive modelling, automation of routine tasks, and real-time quality monitoring. By harnessing AI, pharmaceutical companies can enhance regulatory compliance, streamline documentation, and improve decision-making through intelligent decision support systems. Drug development is a time consuming, expensive, and extremely risky procedure. Up to 90% of drug concept are discard due to challenges such as toxicity safety and efficacy resulting significant loss of investor. AI's capabilities range from enhancing accuracy and minimizing error to enabling previously impossible new ideas. AI powered quality assurance framework, leveraging machine learning, computer vision, and predictive analytics to ensure unparalleled quality excellence.