The Role of AI in Modernizing Clinical Trials: From Patient Selection to Data Management

Abstract

Aim: This study aimed at researching the transformative role that artificial intelligence has played in making clinical trials more efficient and effective-particularly concerning patient selection, trial design, and data management. Purpose: The main objective for this study will be to search, identify, and critically analyze various AI technologies in machine learning, natural language processing, predictive analytics, and their corresponding applications in clinical trials, focusing on using these novel approaches to bypass inefficiencies of the traditional approach and improve the outcomes of a clinical trial. Method: A descriptive research design was used, drawing on a standardized online survey conducted among 200 professionals involved in clinical trials. The survey collected the following: some relatively objective numerical data regarding the use of AI tools and problems encountered while managing data, as well as perceived outcomes from the inclusion of AI in clinical trials. Result: The key findings were that machine learning algorithms dominated with 40%, while the natural language processing aspect represented 30%. Among the challenges noted regarding data management was the integration of data, that reached 35% and quality of data is also at 30%. AI-driven outcomes improved patient selection and the efficiency of trial design. Conclusion: Integration of AI technologies in clinical trials would modernize research practices, meet the challenge of data management, and generally improve the efficiency of a trial. The findings reflect that investment into such tools needs to continue so results from trials can be successful and healthcare solutions are continuously improved.