Enhancing Diagnostic Accuracy in Nigerian Hospitals: Analysis of Deep Learning Applications in Medical Imaging
DOI:
https://doi.org/10.33003/fjs-2026-10(ANB-K)-5193Keywords:
Deep learning, medical imaging, nigeria, artificial intelligenceAbstract
Nigeria faces a critical shortage of radiologists and other medical specialists, creating significant challenges for timely and accurate diagnosis, particularly in underserved and rural healthcare settings. Medical imaging plays a central role in disease detection and management, yet the growing demand for diagnostic services often exceeds available human resources. Recent advances in deep learning, a subset of artificial intelligence, have demonstrated considerable potential in automating image analysis and supporting clinical decision-making. However, the extent of its application and readiness for implementation within Nigerian healthcare institutions remain insufficiently explored. This systematic review examines the current state of deep learning applications in medical imaging relevant to Nigerian healthcare. Specifically, it evaluates diagnostic performance, identifies commonly used deep learning architectures and imaging modalities, assesses implementation challenges, and highlights opportunities for future research and clinical adoption. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed across PubMed, IEEE Xplore, Scopus, and Google Scholar for studies published between 2015 and 2025. Eligible studies reported the development, validation, or application of deep learning models for medical imaging tasks relevant to Nigerian healthcare contexts. Study selection followed predefined inclusion and exclusion criteria.
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