The field of radiology is intrinsically linked to cutting-edge technologies, with the professional community demonstrating a sustained adoption of innovations (1). Radiology plays a crucial role in early diagnosis, staging, and follow-up care. Despite significant progress, challenges remain in clinical efficacy, uniform deployment, and pricing models (1-4). Artificial intelligence (AI) is emerging as a transformative force, optimizing workflow processes, including image acquisition, analysis, and reporting (5). While these advanced technologies hold promise for addressing chronic challenges in accessing quality radiological services in low-income countries like Ethiopia, their benefits are often unevenly distributed, contributing to a global AI divide that exacerbates existing health disparities (6).