Artificial Intelligence in Clinical Trials: Transforming the Future of Drug Development

Abstract

Artificial intelligence (AI) is changing the way clinical trials are planned, carried out, and understood. AI methods like natural language processing, machine learning, and deep learning can make it easier to find and recruit patients, support more flexible and effective trial designs, and keep an eye on safety and effectiveness all the time by using large-scale electronic health records, imaging archives, multi-omics data, and real-world data. These features promise shorter timelines, lower costs, and a better chance that trials will answer clinically important questions. However, significant challenges model opacity and generalizability, bias and fairness, data governance and privacy, and the operational preparedness of sites and sponsors. This review brings together the most recent uses of three main areas: recruitment and retention, predictive analytics and trial design, and monitoring and outcome assessment. It also talks about the ethical and regulatory issues that affect how these tools are used. Moreover, it ends by talking about where integrated, lifecyclespanning platforms, multimodal fusion, explainability, and privacy-preserving collaboration might go in the future. These changes point to AI becoming a key part of clinical research that is more open, efficient, and trustworthy. Artificial intelligence, clinical trials, adaptive design, and digital twins are some of the key terms.