Human--AI Interaction in Low-Tech, Low-Resource Healthcare Systems

Abstract

Low-resource healthcare systems in South Asia and Africa face severe constraints in infrastructure, connectivity, data availability, and digital literacy that shape how artificial intelligence can be deployed. This paper examines human–AI interaction in these settings, focusing on patients, community health workers, and clinicians rather than model performance alone. Drawing on empirical studies and case examples from Bangladesh, India, and Nigeria, it shows that AI systems designed for high-income contexts often fail when transferred without adaptation. Key challenges include language diversity, absence of electronic health records, limited AI literacy, trust deficits, and unresolved ethical and liability concerns. The analysis demonstrates that localized, multilingual, human-in-the-loop AI can meaningfully augment care when integrated into existing workflows and mediated by trusted health workers. Overall, the paper synthesizes design principles for human-centered AI that emphasize localization, explainability, training, and accountability, arguing that effectiveness depends more on interaction design than technical sophistication.