The rise of Artificial Intelligence (AI) has triggered a revolution in the pharmaceutical industry, with the aim of tackling longstanding issues such as high drug development costs, long timelines, and low success rates in drug discovery and clinical trials. Traditional methods of drug development are resource-intensive and often inefficient, creating a strong demand for data-driven and automated solutions. In this review, the authors examine AI applications in drug discovery and clinical trial optimization, highlighting their role in enhancing efficiency, accuracy, and decision-making. Technologies like machine learning and deep learning are becoming increasingly valuable for identifying new drug targets, predicting molecular interactions, and optimizing synthesis pathways. In clinical studies, AI assists with patient recruitment, trial design and testing, real-time monitoring, and predictive analytics of treatment outcomes. Integrating large datasets—including genomics, electronic health records, and imaging data—also supports personalized medicine and improves patient stratification. Key findings suggest that AI can significantly shorten drug development timelines, cut operational costs, and increase the precision of clinical procedures. However, challenges such as data quality, algorithmic bias, legislation and regulation, and difficulties with data sharing still limit widespread adoption. Future efforts will likely focus on enhancing data integration, transparency, and regulatory frameworks to fully realize AI’s potential in pharmaceutical research and healthcare innovation.
Addepalli Lakshmi Abhishek, Rachna Varma, Shweta Anand (Sun,) studied this question.