Citation :

Manoj Boopathi Raj, Sneha Murganoor, 2023. "AI in Personalized Medicine: Tailoring Treatments to Individual Genetic Profiles with Machine Learning" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 1, Issue 1: 82-95.

Abstract :

Personalized medicine is a concept shifting from a conventional system of patient treatment to patient-specific genetic and phenotypic variations. Modern technologies in sophisticated artificial intelligence and machine learning in the healthcare industry have enhanced the possibility of making tailored treatments depending on a client’s genetic makeup. This paper is going to examine the roles of AI, ML and personal medication with a view to analyzing how these technologies are implemented to personalize medicine. In specific, the study gives back and takes a look at different AIMS models, including supervised learning, deep learning and reinforcement learning, that are used in the area of PM to forecast patients’ prognosis, diagnose ailments and recommend the most suitable treatments. Neural network is one of the advanced AI techniques which are capable of functioning on large-scale genomic data and screening out the mutations or biomarkers associated with the diseases. Artificial intelligence also presents capabilities to identify correlations from genomic information that may be used in identifying disease susceptibility, drug reactions or complications from a particular disease. We explain how methodology based on machine learning allows clinicians to identify the likelihood of drug effectiveness and toxicity and, therefore, set more precise routes. In addition, through the help of AI, it enhances pharmacogenomics, the study of genetic factors that influence drug response, making it easier to prescribe doses and treatment plans. Despite the potential that AI in personalized medicine, there are numerous barriers, including data privacy, integration into clinical practice, model interpretability and many more that are still emerging. Cancer, cardiovascular diseases, neurological disorders, and rare genetic makeups are some of the easily recognizable areas that the paper, using case studies and recent developments, seeks to establish how AI is being applied in identifying the treatment channels. Finally, this paper gives a future prospect on the part that artificial intelligence will play in the future execution of health enhancement.

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Keywords :

Artificial Intelligence (AI), Machine Learning (ML), Personalized Medicine, Genomic Data, Pharmacogenomics, Precision Medicine, Predictive Analytics, Deep Learning.