Localized AI for stroke care in LMICs
A framework to overcome structural and diagnostic barriers
Bibliographic Data
| ID | 22076250 |
|---|---|
| Authors | Qing Liu (0000-0002-5792-6037, Southwestern University of Finance and Economics), Xuemei Jia (0000-0002-9323-4425, Southwestern University of Finance and Economics), Yingchun He (0000-0001-8723-160X, Sichuan Academy of Traditional Chinese Medicine), Yufeng Hou (0000-0003-2813-1056, Sichuan Mianyang 404 Hospital), Yulin Deng (0009-0006-6005-0634, Southwestern University of Finance and Economics), Zhi Yan (0000-0001-8251-9786, Capital University) |
| Year | 2026 |
| Volume | 14 |
| Pages | 1859276-1859276 |
| Publication date | 2026-06-10 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2026.1859276 |
| PMID | 42359122 |
| OpenAlex | W7164134331 |
| Language | EN |
| References cited | 82 |
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
Corporate governance · Digital health · Global health · Health care · Public health · Scalability · Software deployment · Workforce · Acute Ischemic Stroke Management · Artificial Intelligence in Healthcare and Education · Stroke Rehabilitation and Recovery · Health Policy
| Highly cited | No |
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