Towards the One Exposome: harnessing artificial intelligence and digital health technologies across One Health toxicology, new approach methodologies, and environmental public health

The One Exposome framework integrates exposomics, One Health toxicology, New Approach Methodologies (NAMs) and artificial intelligence to advance environmental health from reactive risk assessment towards predictive, preventive, and participatory science. The paper positions One Health as the ethical and conceptual rationale for recognizing the interdependence of human, animal, and environmental health, the Exposome as the framework for measuring cumulative and life-course exposures, and NAMs together with digital and AI-enabled tools as the means to generate, integrate, and interpret mechanistic and adverse outcome pathway (AOP)-based evidence across biological scales. In this review, environmental health challenges such as antimicrobial resistance, endocrine disruption, and other complex multi-stressor exposures cannot be adequately addressed through compartmentalized, single-substance, or purely retrospective approaches. The One Exposome paradigm instead proposes a systems-based model that links exposure data, mechanistic toxicology, and public health intelligence across humans, animals, and ecosystems. It draws on human-relevant NAMs, including microphysiological systems, together with AI and multi-omics approaches, to support cross-species interpretation, earlier hazard identification, and more preventive intervention strategies. Within this framework, sentinel surveillance, integrated monitoring, and interoperable data systems are central for detecting shared risks across environmental, veterinary, food, and human health domains. The approach is also presented as requiring governance reform so that agricultural practice, environmental management, and clinical stewardship are aligned within a common preventive and planetary health perspective. Overall, the paper frames the One Exposome as a transdisciplinary architecture for environmental public health that combines measurable environments, mechanistic understanding, and computational integration. By connecting cumulative exposures to biological responses and population-level consequences, it offers a foundation for more anticipatory, equitable, and scientifically integrated environmental health decision-making.