Exploring the Potential of Artificial Intelligence in Optimizing Clinical Trial Design for More Efficient Drug Development

Abstract

The clinical trials are important for the development of medical science since it becomes easier to test novel drugs, medical equipment, and even new methods in the treatment of a person. Only 10% of these studies were able to complete the entire process-from original drug design to post-marketing surveillance, which is slightly worrying. This low completion rate seriously jeopardizes the overall sustainability of clinical research and public health and healthcare economics. Increasing study designs and costs, along with other related difficulties in patient recruitment and data management, also work to worsen this problem. In this respect, AI has become a really powerful instrument that can revolutionize several aspects related to clinical trials. Thus, to gauge the effectiveness of AI in the sphere of patient recruitment efficiency, and the accuracy of data management along with meeting deadlines for the development of drugs, this paper would be carried out by a mixed-methods approach. The paper illustrates considerable achievements in the fields associated with AI technologies through qualitative views of experts along with the quantitative analysis of key indicators. According to the findings, AI has decreased input error in data by 25%, cut average development time for medication by 22%, and reduced total identification of patients by 30%. Besides, AI has also been enhanced to predict the efficacy of drugs with the precision of 13%. These outcomes therefore highlight how AI can accelerate the procedures of clinical trials and increase participant diversity, which may, in the long run, influence the outcome of the trail. This may thus open doors for health breakthroughs to be more efficient and timely delivered.