ReviewFrontiers in public health2026
Localized AI for stroke care in LMICs: a framework to overcome structural and diagnostic barriers.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Low- and middle-income countries (LMICs) bear a disproportionate share of the global stroke burden, driven not only by resource limitations but also by systemic inefficiencies in workforce distribution, diagnostic access, and prehospital care coordination. While advances in artificial intelligence (AI) have demonstrated significant potential in stroke diagnosis and management, many existing solutions remain poorly aligned with the infrastructural and policy realities of LMIC health systems, limiting their scalability and long-term impact. This study presents a comprehensive narrative review of literature published between January 2015 and March 2026, synthesizing evidence across digital health, stroke systems of care, and AI deployment models. We identify three persistent structural barriers-workforce shortages, diagnostic centralization, and fragmented care pathways-that collectively constrain timely intervention in acute stroke. In response, we propose a "Localized AI + Policy" framework that integrates lightweight AI models, edge computing, and federated learning within context-specific health system and governance structures. This approach emphasizes decentralized computation, data sovereignty, and alignment with national health policies, enabling more resilient and scalable deployment of AI in resource-constrained environments. By shifting the focus from technology-centric innovation to system-integrated implementation, this framework highlights a pathway for translating AI advances into sustainable public health impact. The findings underscore the importance of embedding digital health solutions within broader strategies for health system strengthening, universal health coverage, and global health equity.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.