[Artificial Intelligence in the Pharmaceutical Industry: Governance, Quality, and Regulatory Challenges]

Artificial intelligence (AI) is progressively transforming the pharmaceutical industry by impacting manufacturing, quality assurance, regulatory affairs, supply chain management, and governance of industrial systems. The objective of this review is to provide a critical synthesis of the applications, limitations, and regulatory challenges associated with the integration of AI into regulated pharmaceutical environments.

This narrative review is based on a corpus of more than forty recent academic publications, institutional reports, and regulatory documents covering pharmaceutical manufacturing, Good Manufacturing Practices (GMP), quality assurance, regulatory affairs, supply chain management, data governance, organizational adoption, and sustainability.

AI contributes to process optimization, predictive maintenance, real-time quality control, document automation, regulatory decision support, and improved supply chain performance. It also promotes the emergence of more connected and adaptive production models within the framework of Pharma 4.0. However, these benefits remain highly dependent on data quality and interoperability, model validation, system explainability, cybersecurity, GMP constraints, governance requirements, and the control of generative AI use in regulated environments.

AI represents a major driver of transformation in the pharmaceutical industry, but its value depends on its integration into robust industrial, organizational, and regulatory frameworks. The development of pharmaceutically credible AI requires systems that are validatable, governable, auditable, and compatible with the trust requirements inherent to healthcare activities.