Convolutional neural networks for medical imaging in resource-constrained settings: a scoping review of architectures, performance, and deployment challenges.

Background: Convolutional neural networks (CNNs) have emerged as powerful artificial intelligence tools for medical image analysis, demonstrating substantial improvements in disease detection, classification, and diagnostic support. Despite increasing evidence regarding their clinical performance, implementation within resource-constrained healthcare environments remains challenging due to limitations in infrastructure, computational resources, dataset availability, and healthcare workforce capacity. Understanding the current evidence relating to CNN architectures, performance characteristics, and deployment barriers is necessary to support sustainable implementation in underserved healthcare settings. Aim: This review aimed to critically map the available evidence regarding convolutional neural networks for medical imaging in resource-constrained settings, with emphasis on CNN architectures, diagnostic performance, and deployment challenges. Methods: This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guideline. Literature searches were conducted across PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect from database inception to April 2026. Search terms combined Medical Subject Headings (MeSH) and free-text keywords using Boolean operators. Eligibility criteria were developed using the population-concept-context (PCC) framework. Peer-reviewed English-language studies investigating CNN applications in medical imaging relevant to resource-constrained healthcare settings were included. Data extraction and synthesis were performed thematically. Results: A total of 18 studies met the inclusion criteria. Five major themes were identified: diagnostic accuracy and clinical performance of CNN models; lightweight and resource-efficient CNN architectures for low-resource deployment; explainability and interpretability of CNN systems; transfer learning and optimization for limited datasets; and implementation barriers affecting deployment in resource-constrained settings. CNN systems demonstrated strong diagnostic performance across tuberculosis, pneumonia, malaria, skin cancer, and other imaging applications, with several studies reporting near expert-level performance. Lightweight architectures and transfer learning approaches improved computational feasibility, while explainability methods enhanced transparency and clinician confidence. However, implementation challenges including computational limitations, poor connectivity, dataset scarcity, and algorithmic bias persisted across studies. Conclusion: CNN-based medical imaging systems demonstrate considerable potential for strengthening diagnostic capacity within underserved healthcare environments. Nevertheless, sustainable implementation requires greater emphasis on context-specific model development, explainability, locally representative datasets, and real-world deployment studies to ensure equitable and effective adoption in resource-constrained healthcare systems.