Enhancing Treatment Efficacy Through Genomic Data Analysis

EasyChair Preprint 14408

Abstract

AI-driven personalized medicine is revolutionizing healthcare by tailoring treatments to individual patients based on their unique genomic data. This approach leverages advanced machine learning algorithms to analyze vast datasets of genetic information, enabling the identification of specific biomarkers and genetic variations associated with diseases. By integrating this genomic data with clinical records and environmental factors, AI models can predict patient responses to various treatments, thereby enhancing the efficacy of therapeutic interventions. This personalized strategy not only improves patient outcomes but also minimizes adverse effects by selecting the most suitable treatments

Keyphrases: - **Artificial Intelligence (AI)**, - **Clinical Applications**, - **Complex Diseases**, - **Data Privacy**, - **Ethical Considerations**, - **Future Trends**, - **Gene Editing**, - **Genetic Markers**, - **Genomic Data**, - **Healthcare Innovation**, - **Immunotherapy**, - **Machine Learning**, - **Multi-Omics Integration**, - **Personalized Medicine**, - **Pharmacogenomics**, - **Precision Medicine**, - **Predictive Analytics**, - **Rare Diseases**, - **Real-World Examples**, - **Targeted Therapy**

@booklet{EasyChair:14408,
  author    = {Wayzman Kolawole},
  title     = {Enhancing Treatment Efficacy Through Genomic Data Analysis},
  howpublished = {EasyChair Preprint 14408},
  year      = {EasyChair, 2024}}