Infectious diseases (IDs) remain a major threat to global health and societal stability. Because most emerging IDs in humans are zoonotic in origin and shaped by environmental contexts, effective prevention and control call for a One Health approach. Machine learning is widely used for ID modelling and forecasting but often lacks interpretability to explain predictions or guide public health action. Explainable AI (XAI) makes complex models interpretable, enabling attribution of predictions and identification of key outbreak drivers. In this Personal View, we argue that embedding XAI within a One Health framework offers a new organising principle for ID intelligence. We highlight emerging applications in surveillance and forecasting, zoonotic spillover, antimicrobial resistance monitoring and optimisation of resource allocation. We also outline key challenges, including data harmonisation, governance, privacy protection and equitable distribution of risks and benefits. Advancing XAI-enabled One Health systems will require collaboration across sectors and methodological innovation.
Cao et al. (Thu,) studied this question.