A Systematic Review of AI-Driven Innovations in Drug Development, Precision Medicine, and Healthcare

Review Article

Abstract

Artificial Intelligence (AI) is a huge transformation in pharmaceutical research and healthcare delivery, changing traditional approaches to drug development and patient care. This review discusses about the use of AI across the pharmaceutical value chain, from early-stage drug discovery to personalized therapeutic interventions. The implementation of machine learning algorithms has significantly enhanced target identification, molecular design, and clinical trial optimization, reducing both time and cost investments in drug development. In the realm of precision medicine, AI applications have advanced patient stratification, treatment response prediction, and pharmacogenomic analyses, enabling more personalized therapeutic strategies. The review also discusses emerging trends in deep learning, natural language processing, and generative AI models, which have demonstrated remarkable potential in drug discovery and healthcare applications. The adoption of explainable AI frameworks has addressed transparency concerns, while conversational AI has improved patient engagement and healthcare delivery. Additionally, AI applications in pharmaceutical manufacturing have optimized production processes and quality control measures. However, challenges remain in data governance, privacy protection, and ethical considerations. All these findings indicate that AI has substantially improved efficiency and accuracy in drug development and healthcare delivery, while demanding the need for continued development of robust guidelines for responsible AI implementation in clinical settings.