AI-Driven Digital Quality and Compliance: Transforming GXP Systems in Life Sciences through Intelligent Automation

Abstract

As life science businesses must deal with an ever-increasing abundance of regulations, traditional Good Practice (GxP) compliance methods, often manual, uncoordinated, and resource-intensive, are unable to adapt to the rapidly changing landscape of regulations. This paper reviews how Artificial Intelligence (AI) and Intelligent Automation are used revolutionize digital quality and compliance systems. In particular, it describes how the traditional Computer System Validation (CSV) model correlates to the new risk-based Computer Software Assurance (CSA) methodology and how this methodology includes continuous validation and compliance-by-design. The paper analyzes the key AI technologies utilized in quality management, manufacturing, clinical trials, and pharmacovigilance, including machine learning, natural language processing, generative AI, and explainable AI, with respect to their benefits to the quality department, including increased audit-readiness, predictive risk management, and greater efficiency. The paper also discusses several key issues, including data integrity, model transparency, regulatory uncertainty, and system integration as they relate to the AI-based compliance model. Finally, the paper concludes that while AI provides an innovative method to improve compliance agility and scalability, successful implementation will require a comprehensive governance structure, an emphasis on explainability, and adherence to the changing regulatory environment in order to reshape the future of GxP compliance methods in the life sciences.