The Digital Transformation of Diagnostic Imaging: A Narrative Review on Cloud-Based Platforms, Artificial Intelligence Integration, and Global Networks in Modern Teleradiology

Abstract

Background: Teleradiology has evolved from a niche solution for after-hours coverage to a foundational component of modern radiology practice, accelerated by technological innovation and globalized healthcare demands. The convergence of cloud computing, artificial intelligence (AI), and sophisticated data networks is fundamentally reshaping how medical images are stored, analyzed, and interpreted across geographical and institutional boundaries.

Aim: This narrative review aims to critically synthesize contemporary evidence (2010-2024) on the technological, operational, and professional trends transforming teleradiology.

Methods: A comprehensive literature search was conducted across PubMed, IEEE Xplore, Scopus, and the Journal of Digital Imaging archives.

Results: The transition from on-premise Virtual Private Networks (VPNs) to scalable, vendor-neutral cloud platforms enables unprecedented flexibility, disaster recovery, and multi-institutional collaboration. These trends facilitate the emergence of global radiology reading networks, 24/7 subspecialty coverage, and integrated diagnostic hubs. However, critical challenges persist regarding data sovereignty, cybersecurity resilience, inconsistent regulatory frameworks, liability attribution in AI-assisted reads, and the potential erosion of the traditional radiologist-patient-clinician triad.

Conclusion: Teleradiology is undergoing a profound paradigm shift from a simple image transmission service to a complex, AI-enhanced, cloud-hosted ecosystem. Future success requires robust international data governance frameworks, standardized AI validation protocols, and a redefinition of radiologist roles within distributed diagnostic networks to ensure these technological advancements translate into equitable, accurate, and secure patient care.