ROLE OF ARTIFICIAL INTELLIGENCE IN PERSONALIZED MEDICINE A COMPREHENSIVE REVIEW

Abstract

The idea behind personalized medicine is simple to explain but difficult to execute: prevention, diagnosis, prognosis and treatment should be based on the biological, clinical, environmental and behavioral specificities of the patient who is in front of the clinician, rather than the average patient of a trial population. In fact, one of the key technologies driving this goal is artificial intelligence (AI), which can manage data that is often too big, too complex, or too multidimensional to be comfortably handled by traditional statistical methods. Nevertheless, AI has not fully matured and is still experiencing its adolescence in the field of personalized medicine in the domains of biomarker discovery, disease subtyping, risk prediction, early diagnosis, treatment-response modelling, pharmacogenomics, digital pathology, longitudinal monitoring, and clinical decision support.t outlines the conceptual foundations, types of data that enable personalization, key clinical applications, ethical and legal issues yet to be resolved, and obstacles between potential research and clinical practice. The perpetual debate is that AI does not take over the doctor's decision-making, but rather helps doctors to access more information than they could retain within their minds all at once.