Localized AI for stroke care in LMICs: a framework to overcome structural and diagnostic barriers

Area of Science:

• Global Health
• Medical Informatics
• Public Health Policy

Background:

• Low- and middle-income countries (LMICs) disproportionately affected by stroke due to resource limits and systemic inefficiencies.
• Existing artificial intelligence (AI) solutions for stroke care often misalign with LMIC infrastructural and policy realities, hindering scalability.
• Persistent barriers include workforce shortages, centralized diagnostics, and fragmented care pathways, delaying acute stroke interventions.

Purpose of the Study:

• To review literature on digital health, stroke systems of care, and AI deployment in LMICs.
• To identify structural barriers limiting timely stroke intervention in LMICs.
• To propose a novel framework for scalable AI deployment in resource-constrained stroke care.

Main Methods:

• Comprehensive narrative literature review (January 2015 – March 2026).
• Synthesis of evidence on digital health, stroke care systems, and AI implementation models.
• Identification and analysis of structural barriers in LMIC stroke care.

Main Results:

• Three key structural barriers identified: workforce shortages, diagnostic centralization, and fragmented care pathways.
• A "Localized AI + Policy" framework is proposed, integrating lightweight AI, edge computing, and federated learning.
• The framework emphasizes decentralized computation, data sovereignty, and alignment with national health policies for LMICs.

Conclusions:

• The "Localized AI + Policy" framework offers a pathway for sustainable AI implementation in resource-constrained stroke care.
• Decentralized AI approaches are crucial for overcoming LMIC health system challenges.
• Integrating digital health solutions into health system strengthening is vital for global health equity and universal health coverage.