The Impact of Artificial Intelligence on Clinical Trials in the Pharmaceutical Industry

This research investigates the potential of artificial intelligence (AI) in clinical trials, focusing on efficiency, accuracy, and cost-effectiveness. To assess current systems and AI's impact, a comprehensive literature review and a structured quantitative survey targeting pharmaceutical professionals were employed. Data were analyzed using statistical tools such as SPSS and Minitab. The primary objective is to understand the efficiency of current systems and how AI can streamline current systems. By understanding both, this research proposes AI as a tool to help significantly reduce the costs and time associated with clinical trials. The research integrates a thorough literature review and a quantitative survey. The survey was distributed to pharmaceutical professionals and included Likert-scale questions and open-ended responses. Data analysis involved descriptive statistics and inferential statistics, including t tests and regression analysis. The survey results indicate a moderate positive correlation between the perception of AI and the perceived efficiency of clinical trials. A significant positive correlation was found between the perceived potential of AI and the overall efficiency of clinical trials. However, no significant difference was observed in AI perception and its impact between pharma and non-pharma professionals. To conclude, AI has the potential to significantly enhance clinical trial efficiency, particularly in patient recruitment, data management, and trial design. Despite the perceived barriers to adoption, the overall positive perception of AI suggests a promising integration into clinical trials, with benefits for the industry.