Abstract
<jats:p>Artificial intelligence (AI) is rapidly transforming microbiological research by improving pathogen detection, accelerating genomic analysis, enhancing antimicrobial resistance surveillance, and supporting evidence-based public health decision-making. While high-income countries have integrated AI into advanced laboratory systems, many African laboratories continue to face persistent challenges, including inadequate infrastructure, shortages of highly trained personnel, fragmented data systems, unreliable electricity, and limited financial resources. Despite these constraints, several African countries have demonstrated that strategic investments in digital technologies, genomic surveillance, and international scientific collaboration can substantially strengthen microbiology research and laboratory capacity.This article examines the role of artificial intelligence in enhancing microbiology research capacity within low-resource African laboratories through a comparative analysis of empirical experiences across South Africa, Senegal, Nigeria, Kenya, Uganda, the Democratic Republic of the Congo (DRC), and the Central African Republic. Rather than presenting AI as a purely technological innovation, the paper conceptualizes it as an institutional and scientific capacity-building instrument whose effectiveness depends on governance quality, human capital development, infrastructure, and collaborative research ecosystems.Drawing upon recent literature, reports from international organizations, and documented laboratory experiences, the study explores the application of AI in diagnostic microbiology, genomic surveillance, antimicrobial resistance monitoring, digital pathology, and drug discovery. Particular attention is devoted to lessons learned from responses to Ebola virus disease, COVID-19, Mpox, tuberculosis, malaria, and antimicrobial resistance. The analysis demonstrates that AI can significantly improve laboratory efficiency, diagnostic accuracy, outbreak detection, and scientific productivity when supported by appropriate institutional arrangements and sustainable investment.The article identifies persistent barriers that continue to constrain AI adoption, including digital inequality, insufficient laboratory infrastructure, limited computational resources, inadequate regulatory frameworks, data governance concerns, and shortages of multidisciplinary expertise. Building on these findings, the paper proposes an African AI Laboratory Capacity Framework comprising six mutually reinforcing dimensions: infrastructure readiness, human capital development, digital integration, data governance, collaborative research networks, and responsible AI governance.The study concludes that the future of microbiology research in Africa depends not merely on acquiring sophisticated AI technologies but on strengthening resilient scientific institutions capable of integrating technological innovation with sound governance, ethical stewardship, and long-term capacity development. By providing empirically grounded policy recommendations, this article contributes to ongoing debates on AI governance, scientific capacity building, and the modernization of laboratory systems in low-resource settings.</jats:p>