AI and Digital Public Infrastructure for Public Health Resilience

The growing frequency and complexity of public health emergencies—from pandemics to climate-driven health threats—place unprecedented strain on health systems. These crises require health systems not only to respond to acute shocks, but also to sustain essential services under prolonged stress and adapt to long-term consequences.Despite rapid advances in artificial intelligence (AI), its application in this domain remains largely fragmented. Most deployments are limited to isolated functions such as prediction, classification, or operational optimization, without integration into the broader system workflows that govern real-time decision-making in emergencies. Consequently, we have limited evidence of AI contributing to system-level capabilities across the full emergency cycle.Crucially, this evidence gap extends beyond long-term outcomes. Even when assessed through proxy or intermediate performance measures, there is little consistent demonstration that AI-enabled interventions improve system responsiveness, operational resilience, or adaptive capacity under stress.This Research Topic advances a different proposition: that we should understand AI not as a collection of isolated tools, but as part of a broader Digital Public Infrastructure (DPI) for public health systems. This infrastructure enables healthcare resilience as an active system capability to anticipate, respond to, and learn from conditions of stress. Achieving this requires integrating data commons, AI commons, and public–private delivery architectures to support coordinated, adaptive, and equitable responses across the full cycle of disruption.This Research Topic focuses on four interconnected domains:o Governance and Trust Frameworks: Approaches to ensuring regulatory and evidentiary ethics, robustness, safety, accountability, and equity in AI deployment—particularly in contexts requiring responsible access to population-level health data.o Integrative Data Ecosystems: Models for building and sustaining data commons that enable federated learning, cross-sector collaboration, and real-time intelligence across institutions and jurisdictions.o Implementation, Improvement, and System Integration: Practical pathways for embedding AI into health system workflows, including governance, workforce adaptation, and alignment with policy and regulatory frameworks. This includes evaluation approaches using proxy outcomes to support the adoption of AI in real-world settings where long-term impact is not yet observable.o New Collaboration Paradigms and AI as Digital Public Infrastructure: Emerging models of human–AI and agent-based collaboration that position AI as an enabling layer within Digital Public Infrastructure, supporting coordinated, adaptive, and scalable public health responses.This Research Topic was launched in collaboration with the 11th International Digital Public Health Conference, a world-leading annual interdisciplinary event on research and innovation in digital public health, organized by University College London. We welcome submissions from speakers, attendees and the broader research community. Topic editors: Dr. Dimitrios Kalogeropoulos, Meike Schleiff, Clara Beatriz Aranda Jan Source: https://www.frontiersin.org/research-topics/83421/ai-and-digital-public-infrastructure-for-public-health-resilience