Cloud-based machine learning for precision medicine: Transforming healthcare delivery in US

Abstract

This article examines how cloud-based artificial intelligence and machine learning technologies address unique challenges within the United States healthcare system to advance personalized medicine implementation. By analyzing substantial data across diverse healthcare settings, the article demonstrates how cloud infrastructure overcomes critical barriers including fragmented electronic health record systems, complex regulatory requirements, and significant economic constraints. These technological solutions enable seamless data integration across previously siloed systems while maintaining strict compliance with multi-layered privacy regulations. Cloud-based platforms democratize access to advanced precision medicine capabilities, allowing community hospitals and rural facilities to implement technologies previously exclusive to elite academic centers. The findings reveal that these techniques not only reduce implementation costs and timelines but also directly improve clinical outcomes, administrative efficiency, and healthcare equity across diverse American populations. By embedding compliance frameworks into technological architecture and enabling sophisticated collaboration models, cloud-based precision medicine creates pathways for more integrated, accessible, and effective healthcare delivery throughout the United States.