Implementing and Scaling Artificial Intelligence in Low-Resourced Radiation Oncology: A Systematic Review of Deployments

Abstract

Abstract Artificial intelligence (AI) has the potential to address workforce shortages and workflow inefficiencies in radiation oncology, particularly in low-resourced settings where limited specialist capacity constrains access to care. This systematic review evaluated AI deployments in radiation oncology in low- and middle-income countries (LMICs) and assessed readiness for safe implementation and scale-up. Following PRISMA 2020 guidelines, six databases were searched from January 2000 through December 2025. Eligible studies were classified using a radiation oncology-specific integration spectrum (Levels 0-4) and evaluated for workflow integration, validation, deployment, and governance characteristics. Eighteen studies met the inclusion criteria. Two studies (11.1%) focused on readiness or governance without clinical AI deployment (Level 0), nine (50.0%) reported task-level applications (Level 1), six (33.3%) demonstrated clinician-supervised workflow integration (Level 2), and one (5.6%) reported cross-stage workflow orchestration (Level 3). No study provided sufficient evidence for classification as Level 4 operation. Validation was predominantly retrospective, and continuous performance monitoring was not reported. AI deployment in LMIC radiation oncology remains concentrated at early stages of integration. This pattern reflects not a lack of innovation, but constraints in validation, workflow maturity, infrastructure, and governance. Safe and scalable deployment will require coordinated advances across these domains.