Paper Contents
Abstract
The integration of artificial intelligence (AI) into genomic medicine has revolutionized the way disease risk is assessed and managed. Personalized risk scores, developed through the analysis of individual genetic data, allow for early prediction and intervention in various diseases. AI algorithms are particularly adept at handling the vast and complex datasets generated from genome sequencing, identifying subtle patterns that may escape conventional statistical techniques. These models consider gene-environment interactions, rare variants, and epigenetic factors, thereby enhancing the precision of risk prediction. As a result, clinicians can offer more tailored preventive strategies, diagnostics, and treatment options. This paper explores the core methodologies, applications, benefits, and ethical considerations surrounding AI-powered personalized risk scoring systems in genomic medicine.
Copyright
Copyright © 2025 Akshatha H U. This is an open access article distributed under the Creative Commons Attribution License.
