Artificial Intelligence and Its Role In The Development Of Personalized Medicine And Drug Control

Abstract

DNA sequencing, imaging procedures, and wireless healthcare monitoring devices are all examples of high-throughput, data-intensive precision medicine assays and technologies that have necessitated new methods for analysing, integrating, and interpreting the enormous volumes of data they produce. While several statistical approaches have been developed to deal with the "big data" generated by such tests, previous experience with artificial intelligence (AI) techniques suggests that they may be especially well-suited. Furthermore, data-intensive biomedical technologies applied to study have shown that people differ greatly at the genetic, biochemical, physiological, exposure, and behavioural levels, particularly with regards to disease processes and treatment receptivity. This indicates the need to 'personalise' medications so that they better suit the complex and often individual needs of each patient. AI can play a significant role in the clinical research and development of new personalised health products, from selecting relevant contribute to sustainable to testing their utility, because of the importance of data-intensive assays in revealing appropriate intervention objectives and approaches for personalising medicines. The work here presents a variety of ways in which AI can contribute to the progress of personalised medicine, and we argue that the success of this endeavour is critically dependent on the improvement of appropriate assays and methods for storing, aggregating, accessing, and ultimately combining the data they generate. In addition, the manuscript also discusses the potential future research directions and highlights the shortcomings of various AI methods.