Deploying Medical AI in Low-Resource Settings: A Scoping Review of Challenges and Strategies

Abstract

<title>Abstract</title> Artificial intelligence (AI) is transforming global healthcare by improving diagnostic accuracy, efficiency, and clinical decision-making. However, its implementation in low-resource settings (LRS) remains constrained by weak digital infrastructure, fragmented data systems, and limited governance capacity. This human-centered scoping review synthesizes recent evidence to identify the main challenges and practical strategies for deploying medical AI in low- and middle-income countries (LMICs). A total of thirty peer-reviewed Q1/Q2 studies published between 2020 and 2025 were analyzed thematically across four domains: digital infrastructure and connectivity, data quality and local capacity, ethics and governance, and policy and sustainability. The findings reveal that successful AI deployment in LMICs depends less on algorithmic sophistication and more on stable systems, trustworthy data, and empowered professionals. Key barriers include unstable electricity, limited internet access, outdated hardware, insufficient AI literacy, and weak governance. Recommended strategies emphasize investing in resilient digital infrastructure, building interoperable data repositories based on HL7/FHIR standards, expanding continuous training programs, establishing fairness audits, and embedding AI governance into national health strategies with sustainable financing. Ultimately, sustainable and equitable medical AI requires embedding human values—transparency, privacy, and equity—into every phase of design and deployment. Meaningful innovation in global health depends on amplifying human judgment with compassionate, trustworthy, and context-aware AI systems, rather than replacing it. The review confirms that properly implemented AI systems can strengthen health service delivery, enhance diagnostic accuracy, and reduce inequalities in access to care.

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