AI-Enabled Precision Medicine: Optimizing Treatment Strategies Through Genomic Data Analysis

Authors

  • Mingxuan Yang Brown University
  • Decheng Huang University of Pennsylvania
  • Haodong Zhang New York University
  • Wenxuan Zheng University of California

DOI:

https://doi.org/10.5281/zenodo.13380619

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n3a10

Keywords:

Artificial intelligence, Precision Medicine, Genomic Data Analysis, Personalized Treatment

Abstract

This research investigates how Artificial Intelligence (AI) can be used in precision medicine to improve treatment strategies by analysing genomic data. We explore sophisticated machine learning methods for examining complicated genomic datasets, such as deep learning models and ensemble techniques. The study focuses on overcoming obstacles in analysing complex genomic data and introduces innovative methods for combining multi-omics data. We create predictive models using AI technology to forecast patient prognosis with better accuracy in predicting disease progression and treatment results. The research also looks into AI's use in finding new uses for drugs, demonstrating how machine learning can speed up the process of finding potential treatments. We introduce a model for personalised treatment planning using AI, which integrates genomic biomarkers and clinical factors to enhance drug combination and dosage selection. The assessment of incorporating AI in clinical decision support systems is conducted, showcasing enhancements in diagnostic accuracy and treatment effectiveness through multiple medical fields. Ethical concerns, such as algorithmic bias and data privacy, are thoroughly examined, focusing on discussing regulatory guidelines for AI in the healthcare sector. Our results suggest precision medicine with AI technology could significantly improve treatment customisation, enhance patient results, and transform healthcare delivery. Nevertheless, it is essential to consider ethical, privacy, and regulatory obstacles when implementing responsible practices.

Author Biographies

Mingxuan Yang, Brown University

Master of Innovation Management and Entrepreneurship, Brown University, RI, USA.

Decheng Huang, University of Pennsylvania

Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA.

Haodong Zhang, New York University

Computer Science, New York University, NY, USA.

Wenxuan Zheng, University of California

Applied Math, University of California, Los Angeles, CA, USA.

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Published

2024-09-01

How to Cite

Yang, M., Huang, D., Zhang, H., & Zheng, W. (2024). AI-Enabled Precision Medicine: Optimizing Treatment Strategies Through Genomic Data Analysis. Journal of Computer Technology and Applied Mathematics, 1(3), 73–84. https://doi.org/10.5281/zenodo.13380619

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