Global Introduction of Artificial Intelligence in the Pharmaceutical Industry

Artificial intelligence (AI) is increasingly transforming all phases of the pharmaceutical product lifecycle. AI and machine learning (ML) tools can handle vast biomedical datasets—far beyond human capacity—and have shown promise in accelerating drug discovery, optimizing clinical trials, improving manufacturing and supply chains, and enhancing safety monitoring and regulatory processes. For example, traditional drug R&D is extremely costly and time-consuming (typically >10 years and ≈US$2.8 billion per new drug), but AI-driven methods (deep learning, predictive models) can rapidly screen libraries and predict ADMET properties, significantly shortening discovery timelines. In clinical development, AI-based platforms are reported to reduce patient recruitment from months to days and enable adaptive trial designs by continuously analyzing trial data. In manufacturing and logistics, AI (digital twins, predictive analytics) optimizes yields and inventory—for instance, Pfizer used AI to boost COVID-vaccine production yield and reduce downtime, while 40% of firms now use AI for demand forecasting and cold-chain monitoring. AI also automates pharmacovigilance by mining electronic health records, literature, and social media for adverse event signals, greatly speeding safety surveillance. Regulators (FDA, EMA) are responding with guidance on AI in submissions and product development. However, adoption faces hurdles: data quality and integration, model interpretability (“black box” issues), ethical/privacy concerns, and evolving regulations. This review surveys the global landscape of AI in pharma, covering drug discovery, clinical trials, manufacturing/supply chain, pharmacovigilance, regulatory affairs, and future prospects. We highlight credible examples and sources from industry reports and peer-reviewed literature to provide a comprehensive, professional overview suitable for pharmacy professionals.