A responsible AI framework for infection surveillance in low-resource settings: ethics, opportunities and threats for LMICs (EOT-LMICs)

The integration of artificial intelligence (AI) into infection surveillance represents a significant shift in public health; however, most AI frameworks and implementation guidance neglect the distinct constraints of low- and middle-income countries (LMICs). This commentary presents the ethics, opportunities, and threats for LMICs (EOT-LMICs) framework, developed through synthesis of the literature on AI adoption, digital health implementation and infection surveillance in resource-limited settings. The framework provides an integrated analytical structure: ethical foundations (equity, data governance, contextual validity) establish preconditions for adoption, opportunities (workforce augmentation, standardised surveillance, outbreak detection) define potential benefits warranting investment and threats (misclassification, infrastructure fragility, model drift, regulatory gaps) identify risks requiring mitigation before and during implementation. Aligned with the 4Ps model (precision, partnership, practice, people), this framework offers policymakers and infection prevention practitioners a pathway for context-appropriate AI implementation that prioritises local needs and human-centred care.