Abstract
AI is transforming the world of pharmaceuticals, spurring innovation in areas like drug discovery, drug development, drug manufacturing, microbiology and personalized medicine. Artificial intelligence (AI) is changing the way drugs are identified, provided, and optimized to ensure patient safety by utilizing advanced technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP). This aids in the faster, accurate identification of drug candidates, enhances clinical trials, and leads to improved therapeutic pipelines by facilitating precise and efficient data analysis. AI helps speed up drug discovery through analyzing vast datasets to also predict molecular interactions and possible side effects. In microbiology, AI is contributing both to identifying antimicrobials and tackling the challenges presented by antimicrobial resistance (AMR). AI applications on patient-data (such as genetic information, lifestyle choice, and clinical histories) can contribute in delivering personalized medicine that enhances the efficacy of treatment by providing information for exposures and reducing negative outcomes. In addition, it strengthens pharmacovigilance initiatives through real-time analysis of drug safety in the post-market space, early detection of ADRs, and risk assessment to address safety concerns for patients. AI has also been a game-changer in various pharmaceutical manufacturing areas where AI accelerates the production process and improves product uniformity. Artificial intelligence-based algorithms are maximizing automation, making supply chains more efficient, and reinforcing quality management systems. AI can track manufacturing processes in real time by examining data from sensors and equipment, allowing it to identify and address problems before they arise, leading to more efficient and manufacturing processes. However, there are still substantial barriers to the widespread adoption of AI in healthcare. One pressing issue is how to tailor AI technologies to healthcare systems that, in general, have quality and standardized datasets to provide the best possible outcomes. Challenges associated with data privacy, regulatory frameworks, and the need for transparent and interpretable AI models persist. The obstacles remain, but the potential to transform drug development, manufacturing, and patient care is immense with AI. This review discusses the expected impacts of AI in the pharmaceutical practices and the challenges ahead to be able to unlock these benefits from AI in a health-care setting. Key Words: Artificial Intelligence, Precision Medicine, Drug Discovery, Machine Learning, Pharmacovigilance
Published Version
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