ENHANCING PERSONALIZED MEDICINE THROUGH ARTIFICIAL INTELLIGENCE AND GENOMIC DATA

Authors

VEERAVARAPRASAD PINDI

Abstract

Personalized medicine aims to tailor healthcare treatments to individual patients based on their unique genetic makeup, lifestyle, and environment. The advent of artificial intelligence (AI) and machine learning (ML) has significantly advanced the field of personalized medicine by enabling the analysis of vast amounts of genomic data to identify patterns and make precise predictions about disease risk and treatment efficacy. This paper explores the integration of AI and genomic data in enhancing personalized medicine, highlighting key methodologies, findings, and future directions. We systematically review recent advancements in AI-driven personalized medicine, including the use of logistic regression, support vector machines (SVMs), deep learning models, and hybrid approaches. Key findings demonstrate that ML algorithms can substantially improve disease risk prediction, treatment personalization, and the integration of multi-omics data. Despite the promising results, challenges such as data quality, algorithmic bias, and data privacy need to be addressed. This research contributes to the field by proposing innovative solutions to enhance the reliability, accuracy, and accessibility of AI-driven personalized medicine. The paper concludes by emphasizing the potential of AI to revolutionize personalized medicine and the need for ongoing research and interdisciplinary collaborations to overcome current limitations.

Keywords

Machine learning, predictive analytics, patient care management, healthcare, data analytics.

ENHANCING PERSONALIZED MEDICINE THROUGH ARTIFICIAL INTELLIGENCE AND GENOMIC DATA. VEERAVARAPRASAD PINDI. 2017. IJIRCT, Volume 3, Issue 1. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2407072