Artificial Intelligence-Enabled Next-Generation Clinical Trials: Improving Drug Development Efficiency and Adaptive Trial Design

Clinical trials play a vital role in evaluating the safety and effectiveness of new medical treatments. However, conventional trial methods often face several challenges, including high operational costs, extended timelines, and difficulties in recruiting appropriate participants (6). In recent years, Artificial Intelligence (AI) has emerged as a promising technology capable of addressing many of these limitations (1,2). AI-based tools can rapidly process large volumes of medical data, identify suitable patient populations, and support more efficient trial management (3,4). Additionally, machine learning techniques can assist researchers in predicting treatment responses and detecting potential safety concerns at earlier stages (5,17). Despite these advantages, issues related to data privacy, algorithm transparency, and ethical considerations remain important topics of discussion (16,19). This review explores current applications of AI in clinical trials, its benefits in improving research efficiency, and the potential future direction of AI-driven clinical research. In countries such as India, where healthcare data is extensive yet often fragmented, the integration of AI could significantly enhance the drug development process.

Artificial Intelligence, Clinical Trials, Machine Learning, Drug Development, Digital Health

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