Integrating Cloud Computing and Big Data Analytics in AI Driven Genetic Research: Enhancing Healthcare Management Through Predictive Modelling and Personalized Treatment Strategies

Abstract

The integration of cloud computing and big data analytics in AI-driven genetic research is revolutionizing healthcare management 
by enabling predictive modelling and personalized treatment strategies. This study explores how machine learning algorithms 
analyze vast genomic datasets to identify disease-associated genetic markers, enhance diagnostic accuracy, and optimize therapeutic 
interventions. Cloud computing provides a scalable and secure infrastructure for processing large-scale genetic data, facilitating 
real-time collaboration among researchers and clinicians while ensuring data privacy and regulatory compliance. AI-powered 
predictive analytics supports early disease detection, risk assessment, and targeted drug development, advancing precision medicine 
and improving patient outcomes. Additionally, this research highlights the challenges of data interoperability, ethical considerations, 
and algorithmic transparency in AI-driven genomic research. By leveraging cloud-enabled AI and big data analytics, the healthcare 
industry can enhance decision-making, accelerate medical discoveries, and deliver more effective, patient-centric treatment solutions.