Evidence on the Utility of Artificial Intelligence in the Interpretation of Diagnostic Radiological Images in Low and Middle-Income Countries: A Scoping Review.
Authors
Affiliations (3)
Affiliations (3)
- Universidad de la Sabana, School of Medicine, Campus Universitario Puente del Común, Km 7 North Highway of Chía, Cundinamarca 250001, Colombia (J.J.V.M., J.M.N.P., E.H.H.R.). Electronic address: [email protected].
- Universidad de la Sabana, School of Medicine, Campus Universitario Puente del Común, Km 7 North Highway of Chía, Cundinamarca 250001, Colombia (J.J.V.M., J.M.N.P., E.H.H.R.). Electronic address: [email protected].
- Universidad de la Sabana, School of Medicine, Campus Universitario Puente del Común, Km 7 North Highway of Chía, Cundinamarca 250001, Colombia (J.J.V.M., J.M.N.P., E.H.H.R.). Electronic address: [email protected].
Abstract
Access to diagnostic imaging in low- and middle-income countries (LMICs) is limited by scarce equipment, geographic barriers, weak digital infrastructure, and shortages of trained personnel. Artificial intelligence (AI) has emerged as a promising tool to mitigate these gaps by improving diagnostic accuracy, assisting non-specialist health workers, and optimizing workflows. This scoping review aimed to synthesize current evidence on the use of AI for interpreting radiological diagnostic images in LMICs. A scoping review was conducted in July 2025 following Arksey and O'Malley's framework and PRISMA-ScR guidelines. Searches were performed in PubMed, Scopus, and Clinical Key for studies published between 2000 and July 2025 in English and Spanish. Eligible studies included clinical applications of AI in radiological imaging within LMICs, reporting relevant outcomes. From 620 records, 51 studies conducted across 33 LMICs were included. Most were published between 2022 and 2025 and focused on ultrasound, X-ray, and computed tomography. AI consistently improved diagnostic sensitivity, specificity, and applicability, particularly for tuberculosis, pneumonia, obstetric care, and oncologic screening. Magnetic resonance imaging showed promising yet mostly experimental evidence, while mammography research remained scarce. Frequent limitations included small sample sizes, single-center designs, reliance on public datasets, and limited multicenter validation. AI demonstrates significant potential to enhance the interpretation of diagnostic radiological images in LMICs, with consistent gains in sensitivity, specificity, and applicability across modalities such as ultrasound, X-ray, and computed tomography. Several studies also reported improvements in workflow efficiency and support for non-specialist providers, underscoring AI's dual role as a diagnostic and operational tool. Nonetheless, methodological heterogeneity and infrastructural challenges highlight the need for multicenter validation and context-adapted implementation strategies to ensure sustainable integration.