Reimagining Radiology in the Artificial Intelligence Era: The Global South Radiology AI Implementation Framework

Abstract

Abstract Artificial intelligence (AI) has matured from experimental models into clinical tools spanning the medical imaging lifecycle. While foundational deep learning demonstrates high diagnostic accuracy, the central challenge for radiology has shifted from algorithmic development to complex, real-world clinical implementation. This challenge is particularly acute across the Global South, where uneven diagnostic access and radiologist shortages pose distinctive barriers while simultaneously offering disproportionate opportunities for health-system transformation. Existing implementation science frameworks often lack the specific operational focus required for AI deployment in resource-variable radiology environments. This invited review introduces the Global South Radiology AI Implementation Framework (GSRAIF), a literature-derived synthesis designed to guide the safe, equitable, and sustainable deployment of AI in medical imaging. The framework was developed through a narrative synthesis of 30 anchor references, following a structured literature search across PubMed, Scopus, and Google Scholar (2017–2026) targeting AI, implementation science, and Global South workflow efficiency. GSRAIF provides actionable guidance for clinical leaders and policymakers, organized around seven implementation requirements captured in the RADIANT acronym: Readiness Assessment, Accountability and Regulation, Data Quality and Governance, Integration into Workflow, Access and Infrastructure, Networked Workforce Development, and Trust, Sustainability, and Scale. Ultimately, realizing the clinical value of AI in radiology will require rigorous continuous learning loops and a decisive shift toward implementation excellence as the primary differentiator of successful health systems.