Personalized Medicine: Leveraging AI to Tailor Pharmacotherapy

EasyChair Preprint 14727

Abstract

Personalized medicine represents a paradigm shift from traditional one-size-fits-all approaches to a more tailored healthcare model that considers individual genetic, environmental, and lifestyle factors. This article explores the integration of Artificial Intelligence (AI) into personalized pharmacotherapy, highlighting how AI technologies enhance the customization of treatment plans. AI's ability to analyze large and complex datasets—encompassing genetic profiles, clinical histories, and lifestyle information—enables more precise drug selection, dosage optimization, and outcome prediction. The article examines key areas where AI contributes to personalized medicine, including genetic data analysis, multi-omics integration, predictive modeling, and real-time treatment adjustments. It also addresses the benefits of AI in improving treatment efficacy, reducing trial-and-error approaches, and increasing patient satisfaction. However, the integration of AI presents challenges such as data privacy concerns, the need for system compatibility, and addressing ethical considerations. Looking forward, the article outlines future trends in AI-driven personalized medicine, including advancements in AI technologies, the expansion of personalized care to include broader data sources, and the importance of interdisciplinary collaboration in advancing research. The promise of AI in personalized medicine lies in its potential to revolutionize pharmacotherapy by delivering more effective, individualized treatments that enhance overall patient care and outcomes.

Keyphrases: Artificial Intelligence (AI), Genetic factors, Multi-omics integration, Pharmacotherapy, data analysis, personalized medicine

@booklet{EasyChair:14727,
  author    = {Joseph Oluwaseyi},
  title     = {Personalized Medicine: Leveraging AI to Tailor Pharmacotherapy},
  howpublished = {EasyChair Preprint 14727},
  year      = {EasyChair, 2024}}