Harnessing AI in Personalized Medicine: A Data-Driven Approach to Predicting Genetic Disorders for Business and Management

Abstract

Personalized medicine has the potential to revolutionize healthcare by tailoring treatments to individual patients based on their unique genetic makeup and medical history. One of the promising applications of Artificial Intelligence (AI) in this field is the prediction of genetic disorders from medical imaging data. This paper explores how AI, particularly machine learning (ML) and deep learning (DL) techniques, can be used to analyze medical imaging data—such as MRI scans, CT scans, and X-rays—to identify genetic predispositions to various disorders. By leveraging large datasets of annotated medical images, AI models can be trained to detect subtle patterns in imaging data that may indicate genetic abnormalities or susceptibilities to conditions like cancer, cardiovascular diseases, or neurodegenerative disorders. The study reviews the existing literature on AI-driven image analysis for genetic disorder prediction, highlighting key algorithms and their applications in clinical settings. It also examines the challenges associated with integrating AI into personalized medicine, including data privacy concerns, model interpretability, and the need for large, diverse datasets. The paper concludes by discussing the future potential of AI in enabling early detection and personalized treatment plans, ultimately improving patient outcomes and advancing precision medicine.