Transforming Clinical Trials with Informatics and AI/ML: A Data-Driven Approach

Abstract

Clinical trials play a crucial role in advancing medical research and healthcare by evaluating the safety and efficacy of new interventions. However, the traditional approach to conducting clinical trials is often time-consuming, resource-intensive, and prone to inefficiencies. In recent years, there has been a growing interest in leveraging informatics and artificial intelligence/machine learning (AI/ML) techniques to revolutionize the clinical trial process. This paper explores the transformative potential of informatics and AI/ML in optimizing various stages of clinical trials, including participant recruitment, trial design, data collection and analysis, and decision-making. By harnessing the power of big data, predictive modeling, natural language processing, and other AI/ML tools, researchers and healthcare professionals can enhance trial efficiency, improve patient outcomes, and accelerate the discovery of new treatments. The integration of informatics and AI/ML has the potential to reshape the landscape of clinical trials, leading to more personalized, adaptive, and data-driven approaches that ultimately benefit both patients and the medical community.