OBJECTIVES:
To systematically map the extent and nature of research on AI-enhanced point-of-care (POC) and rapid diagnostic technologies for infectious diseases in resource-limited settings, and to identify gaps in disease coverage, geographic representation and validation rigour.
METHODS:
This scoping review followed JBI methodology and PRISMA-ScR guidelines. The protocol was registered on OSF (https://doi.org/10.17605/OSF.IO/KV8MP). Five databases (PubMed, Embase, Scopus, Web of Science and IEEE Xplore) were searched for studies published from January 2015 to March 2026. Title/abstract and full-text screening used rule-based keyword screening with manual validation (Cohen's kappa = 0.856). Data were extracted using a 19-variable charting form and enriched with PubMed Central full texts.
RESULTS:
From 1072 records, 551 remained after deduplication and 237 studies were included. Publication volume grew exponentially, with 44% published in 2025-2026. COVID-19 (32%), malaria (27%) and tuberculosis (14%) dominated; neglected tropical diseases accounted for fewer than 8%. Microscopy (21%), molecular diagnostics (17%), biosensors (14%) and rapid diagnostic tests (14%) were the most common modalities. Convolutional neural networks predominated (26%), followed by random forests (10%) and support vector machines (8%). Only 7% of studies reported prospective field validation, while 62% did not report validation level. Geographic analysis revealed concentration in East Africa and South Asia, with underrepresentation of West Africa and Latin America.
CONCLUSIONS:
AI-enhanced POC diagnostics for infectious diseases in resource-limited settings is a rapidly growing field facing critical gaps in validation rigour, disease equity and geographic representation. Only 16 of 237 studies (6.8%) report prospective field validation. Future research should prioritise field validation, expand beyond the COVID-19/malaria/TB triad and involve end-user communities from the design stage.
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