The Role of AI and Machine Learning in Pharmaceutical Data Analysis: A Comprehensive Review

Abstract

The pharmaceutical industry is undergoing a profound transformation driven by the exponential growth of biomedical data and the rapid advancement of artificial intelligence (AI) and machine learning (ML). This review provides a comprehensive analysis of the current landscape of AI/ML applications in pharmaceutical data analysis, spanning drug discovery, development, pharmacovigilance, precision medicine, and supply chain management. We examine the diverse AI/ML techniques employed, including supervised and unsupervised learning, deep learning, and natural language processing, and evaluate their impact on key application areas such as target identification, clinical trial optimization, and personalized treatment. We address the critical challenges related to data quality, interpretability, and ethical considerations, and highlight the opportunities for innovation through federated learning, explainable AI, and robust data governance. This review underscores the transformative potential of AI/ML in accelerating drug discovery, improving patient outcomes, and optimizing pharmaceutical operations. By synthesizing current literature and identifying future trends, we aim to provide a valuable resource for researchers, clinicians, and industry professionals seeking to leverage AI/ML to advance pharmaceutical innovation and improve global health.