Abstract

Artificial Intelligence (AI) is transforming the drug development and Clinical Trials by improving efficiency, accuracy, and decision-making. AI predicts Pharmacokinetic (PK) and Pharmacodynamic (PD) properties, automates compound screening and enhances clinical testing throughput. In trial design, AI optimizes patient stratification and outcome prediction by analyzing vast datasets from previous trials and electronic health records, leading to cost-effective and adaptive trials. AI also facilitates real-time data monitoring, identifying discrepancies early to ensure data integrity and regulatory compliance. By integrating diverse data sources it streamlines clinical operations, reducing human error and manual workload. However, challenges persist in data quality and integration due to varying standards across sources, necessitating advanced harmonization techniques. Regulatory frameworks often lag behind AI advancements, creating uncertainty and potential delays. Ethical concerns, including patient privacy and data security, must also be addressed for responsible AI implementation. Establishing standardized protocols and ensuring regulatory alignment are critical for AI’s successful integration into clinical research. In conclusion, AI revolutionizes drug development and clinical trials, enhancing efficiency and accuracy. However, overcoming data, regulatory, and ethical challenges is essential for its widespread adoption.