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This book examines how artificial intelligence is being used for epidemic preparedness in the lived realities of the Global South-where infrastructure gaps, fragmented data systems, and unequal governance shape what "innovation" can actually do. Moving beyond algorithmic hype, the chapters analyze real deployments and prototypes in resource-constrained settings, spanning deep learning for peripheral blood smear analysis, AI-enabled tuberculosis screening, and predictive models for outbreak surveillance and operational planning. Across these cases, the volume shows how technical performance is inseparable from questions of implementation: data quality, interpretability, workforce capacity, interoperability, and public trust.

Preparedness, the book argues, is not merely a computational challenge but a social and political project. Contributors challenge AI-as-neutral-fix narratives by foregrounding equity, accountability, and the colonial legacies embedded in data extraction and model transfer. The volume advances practical frameworks for ethical governance, data sovereignty, and decolonizing AI-emphasizing participatory design, context adaptation, and epistemic pluralism. Bringing together empirical case studies from Africa, Asia, and the Middle East, it offers researchers, practitioners, and policymakers grounded pathways for building epidemic readiness that is effective, locally legitimate, and just.

book explores innovative applications of AI for epidemic preparedness and response in the Global South. Drawing from real-world projects, it addresses the practical, ethical, and technical challenges of deploying AI technologies for public health emergencies in diverse contexts such as Africa, Asia, Latin America, and the Middle East.