Artificial Intelligence for Medical Image Analysis in Low-Resource Settings

Source abstract

Artificial intelligence (AI) for medical image analysis is increasingly being developed for low-resource settings, where specialist expertise, imaging equipment, labelled data, and computing resources may be limited. We systematically reviewed 189 studies published between January 2021 and January 2026 to examine how these constraints relate to imaging modalities, clinical applications, and AI methods in low-resource settings. Each study was also assessed against 15 criteria adapted from the FUTURE-AI principles for trustworthy AI. Shortages of clinical expertise were the most common constraint, and portable or lower-cost imaging was widely used. Transfer learning was the most frequent technical strategy (59%), followed by data augmentation (54%) and efficient model design (36%). Medical-image pretraining and self-supervised learning were each used in 4% of studies, and model compression in 3%. Applications were concentrated in retinal disease, pulmonary disease, antenatal care, parasitic diseases, and cervical screening. Beyond these technical and clinical patterns, the trustworthy AI assessment showed a clear gap between documenting how systems were developed and demonstrating how they performed under relevant local conditions. Subgroup performance was evaluated in 24% of studies, intended users or realistic working conditions in 25%, and model explanations with intended users in 4%. Consistent with this gap, among 15 studies that reported the same model and metric during development and in the local setting, 12 performed worse locally. Future research should prioritise representative local evaluation, intended-user assessment, demonstrated resource suitability, and reproducible research practices.