Artificial Intelligence (AI) is rapidly transforming the pharmaceutical industry through its integration into pharmacovigilance, drug discovery, predictive analytics, and healthcare governance systems. Traditional pharmacovigilance frameworks rely heavily on spontaneous reporting systems and manual signal detection processes, resulting in delayed identification of adverse drug reactions, fragmented healthcare datasets, and under-reporting. AI-driven technologies such as Machine Learning (ML), Natural Language Processing (NLP), and predictive analytics provide scalable solutions capable of improving adverse drug reaction detection, automated signal identification, and clinical decision-making efficiency. This review critically evaluates the role of AI in pharmaceutical systems with specific emphasis on pharmacovigilance, drug discovery, sustainability, ESG integration, ethical governance, and regulatory preparedness. The study also analyses challenges associated with AI implementation, including algorithmic bias, lack of transparency, data privacy risks, and uneven applicability in emerging healthcare systems such as India. The findings demonstrate that AI has the potential to shift pharmaceutical systems from reactive compliance-based frameworks toward predictive and data-driven healthcare infrastructures. However, sustainable implementation requires strong governance mechanisms, explainable AI models, ethical accountability, and continuous human oversight.
Abstract
Share on Mobile
Scan to access on mobile
Keywords
Algorithmic Bias
CIOMS
Clinical Decision Support
Data Privacy
Digital Health
Drug
Discovery
Electronic Health Records
Content
No content available