TITLE:
Artificial Intelligence and Public Health Communication in Africa: A Critical Synthesis of Emerging Evidence and Conceptual Gaps
AUTHORS:
Habeeb Abdulrauf, Abdulmalik Adetola Lawal, Zaynab B. Yusuf, Shalewa Babatayo, Comfort Ademola, Gbemisola Simbiat Odejide, Oluwaseun A. Adekoya, Usman Ayobami
KEYWORDS:
Artificial Intelligence, Public Health Communication, Africa, Surveillance, Chatbots, Social Listening; Genomics, Equity, Decolonial Design
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.13 No.9,
September
24,
2025
ABSTRACT: This critical evidence synthesis interrogates how artificial intelligence (AI) is being integrated into Africa’s public health communication and surveillance and identifies what works, for whom, and under what conditions. Drawing on peer-reviewed and grey literature (2013-2024), we systematically screened >180 records and thematically analyzed a final corpus of 41 documents. The synthesis maps three principal contribution domains: (a) outbreak surveillance and early warning (social listening/NLP and pathogen genomics); (b) health communication and service navigation (chatbots, SMS/WhatsApp–mediated coordination); and (c) operational decision support (e.g., vaccination uptake optimization). Evidence of effectiveness is heterogeneous but non-trivial: retrospective social media signal detection preceded official epidemic reports; a randomized trial in Malawi found a mental-health chatbot improved health-worker wellbeing; and WhatsApp use enhanced real-time immunization coordination. Yet translation from promising pilots to durable systems is constrained by structural barriers, fragile digital/electrical infrastructure, data scarcity and governance concerns (quality, privacy, ownership), regulatory fragmentation, limited local technical capacity, and financing models that foster “pilotitis.” A second layer of gaps is conceptual: applications are rarely grounded in theory, seldom decolonial or equity-centered, and insufficiently adapted to low-resource African languages, risking exclusion of marginalized communities. We argue that realizing AI’s public-health value requires a dual agenda: (1) continue rigorous, context-aware evaluations to strengthen the evidence base; and (2) co-develop enabling ecosystems trusted data stewardship, harmonized and enforceable regulation/ethics, sustainable financing, multilingual NLP, and large-scale capacity building so that AI augments, rather than widens, health equity across the continent. This paper distills an actionable research and policy program to move African AI for public health from isolated exemplars to system-level impact.