Reimagining Radiology in the Artificial Intelligence Era: The Global South Radiology AI Implementation Framework.
- 1Shree Advisory & Consulting, LLC, Cleveland HeightsOhioUnited States.
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.
Related
- Bridging the Artificial Intelligence Translation Gap in Perioperative Pulmonary and Respiratory Complications: A Practical Framework for Institution-Level Implementation
- Perceptions of artificial intelligence for radiographic image accuracy: a cross-sectional survey of radiology personnel
- Augmented Radiology: why map artificial intelligence in imaging from and for the Spanish-speaking world
- Artificial Intelligence in Emergency Radiology: Current Applications, Challenges, and Future Directions.
- Patient expectations for artificial intelligence transparency and agency: A qualitative study and proportionate framework for clinical artificial intelligence implementation
BibTeX
@article{mahankali2026reimagining,
title = {{Reimagining Radiology in the Artificial Intelligence Era: The Global South Radiology AI Implementation Framework.}},
author = {Srikanth Mahankali},
journal = {The Indian journal of radiology \& imaging},
year = {2026},
month = aug,
doi = {10.1055/s-0046-1827801},
url = {https://europepmc.org/article/MED/42921376},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Reimagining Radiology in the Artificial Intelligence Era: The Global South Radiology AI Implementation Framework.
AU - Srikanth Mahankali
PY - 2026
DA - 2026/08/29
JO - The Indian journal of radiology & imaging
DO - 10.1055/s-0046-1827801
UR - https://europepmc.org/article/MED/42921376
AB - 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.
SN - 0971-3026
SN - 1998-3808
ER -