Purpose: This review discovers the transformative impression of Artificial Intelligence (AI) and Machine Learning (ML) on the pharmaceutical industry and healthcare delivery, focusing on drug discovery, clinical pharmacy practice, and operational productivity. Methods: The article inspects the historical development of AI, from early neural models to modern deep learning designs like GANs, RNNs, and Transformers, and assesses their specific requests transversely to the drug life span. Results: AI is publicised to significantly accelerate R&D by detecting drug leads quicker and adjusting clinical trials through patient-specific data analysis. In clinical settings, AI-driven choice support systems boost patient safety by reducing medication errors, predicting adverse reactions, and refining adherence—especially realising a 40% growth in adherence in community pharmacies. Still, technologies such as computer vision are restyling medicine supervision and analytical precision in medical imagination. Challenges: In spite of these benefits, the evolution characteristics sprints with "black box" interpretability, data privacy risks, algorithmic bias, and high implementation charges. Conclusion: This review highlights that despite the fact AI is redesigning pharmacy into an extra detailed and inventive field, its innocuous integration requires a specialised workforce exercise, strong governing agendas, and a constant emphasis on the vital social assembly in patient care.
Gambhir* et al. (Fri,) studied this question.
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