Aug 2026· Veredas do Direito· 0 citations· 21 references
TL;DR
Key applications in diagnostics, disease surveillance, supply chain management, and telemedicine are examined, highlighting how AI improves decision-making, resource allocation, and access to care and the findings suggest that the transformative potential of AI depends on strong governance frameworks, institutional capacity, and locally grounded policies.
Abstract
This paper reconceptualises Artificial Intelligence (AI) as a governance infrastructure within African health systems. Many health systems across Africa face structural constraints, including workforce shortages, fragmented data systems, and limited infrastructure, which undermine service delivery and equity. The paper argues that AI enables a transition from reactive, fragmented systems to predictive, integrated, and data-driven governance models. It examines key applications in diagnostics, disease surveillance, supply chain management, and telemedicine, highlighting how AI improves decision-making, resource allocation, and access to care. At the same time, the study identifies critical challenges, including data privacy risks, algorithmic bias, and technological dependency. The findings suggest that the transformative potential of AI depends on strong governance frameworks, institutional capacity, and locally grounded policies. By positioning AI as a governance infrastructure, the paper contributes to understanding how digital innovation can strengthen resilience, efficiency, and equity in African health systems.
The integration of Artificial Intelligence (AI) into healthcare reveals persistent accountability gaps shaped by system opacity, adaptive model behaviour and distributed responsibility across the clinical and technological ecosystem. As accountability becomes a central principle of AI governance, this article develops...
M. Unver, I. Ogu· The Paris Journal on AI &...· 0 citations
This study delves into key policy issues required for the regulation of Generative AI in high-stakes domains such as enterprise management and clinical health care. As more and more organizations deploy large language models in their tools and systems for automation, decision support, or patient care, questions of data...
Ramesh Kumar Ramu· 2026 7th International Confe...· 0 citations
OBJECTIVE
Artificial intelligence (AI) adoption in global health informatics is accelerating, yet scaling, sustainability, equity, and environmental challenges limit impact, particularly in Low and Middle Income Countries (LMICs).
MATERIALS AND METHODS
Drawing on experience from members of the American Medical Inform...
Yuri Quintana, Titus Schleyer, Arriel Benis et al.· JAMIA Journal of the America...· 0 citations
Artificial intelligence is transforming public health at an unprecedented pace – accelerating disease surveillance, reshaping evidence synthesis, augmenting clinical and policy decision-making, and fundamentally altering how populations interact with health information. Yet this rapid integration has consistently outpa...
Dev Roychowdhury· Discover Public Health· 0 citations
A comprehensive AI readiness hybrid framework, HyFrame‐AI, tailored to the region's unique healthcare landscape by aligning the Capability Maturity Model Integration (CMMI) with the World Health Organization (WHO) and Pan American Health Organization (PAHO) health system dimensions is proposed.
Said Baadel, Toka Hassan, Malaadh Baadel et al.· World Medical & Health P...· 0 citations
Artificial intelligence (AI) is transforming disease surveillance by enabling early outbreak detection, predictive modeling, and real-time analysis of diverse health data sources. These advances can strengthen epidemic preparedness and improve public health decision-making. However, AI adoption has progressed faster th...
Abdullahi Osman Mohamed, A. Afyare, Abdinasir Mohamed Abdi et al.· Frontiers in Digital Health· 0 citations
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