Revolutionizing Exposome Science: AI and Sensor Technologies for Health and Equity

The exposome concept encompasses all environmental exposures that an individual experiences throughout their lifetime. It represents an interdisciplinary paradigm that bridges environmental health, epidemiology, molecular sciences, and any other areas that may influence our health. As climate change, urbanization, and chemical proliferation intensify exposure burdens, exposome science is becoming increasingly vital for understanding environmental health impacts, chronic disease etiology, health equity, and disparities.Traditional approaches, however, have been limited by sparse exposure measurements and siloed data. Recent advances in biosensors, mobile health, and omics technologies—combined with big-data analytics—are helping to address these gaps. There is growing recognition that comprehensive exposome profiling, paired with AI to manage big data, can reveal how combined exposures affect vulnerable communities. This Research Topic builds on the momentum of exposomics in public health, aiming to overcome current limitations through innovations in data collection and analysis.This Research Topic aims to tackle the limited understanding of how the exposome influences health equity and community well-being. Although there is recognition that factors such as pollution, diet, stress, and neighborhood conditions shape disease risk, linking these complex exposures to health outcomes—and inequities in those outcomes—remains a challenge. We aim to highlight how artificial intelligence (AI) and advanced sensor technologies can transform exposome research and public health action. By leveraging wearables, remote sensing, and data science, researchers can capture personal exposure data at an unprecedented scale and granularity. AI-driven analytics can then integrate these multifaceted data streams to uncover exposure-disease relationships and identify drivers of health disparities. This Topic will showcase cutting-edge approaches that are bringing exposomics into a new era, where continuous monitoring and machine learning yield actionable insights to improve population health and equity.This Research Topic seeks to advance holistic exposome science by integrating state-of-the-art technologies, analytical methodologies, and ethical considerations. We welcome contributions that address:• Innovative Measurement Technologies: Development and application of wearable, ambient, and IoT-enabled sensor systems—such as smartphones—for comprehensive monitoring of chemical, physical, and social exposures.• Advanced Data Analytics: Novel machine learning models and bioinformatic tools for analyzing high-dimensional exposome data, including exposome-wide association studies and approaches that link environmental exposures to biological responses across multi-omics datasets.• Data Integration Platforms: Strategies and platforms for merging exposure, omics, and health data to enable holistic and multidimensional exposome assessments.• Applied Case Studies: Real-world applications of exposomics in addressing health disparities, promoting environmental justice, and demonstrating impact on diverse populations.• Ethical, Equity, and Policy Dimensions: Research examining the ethical, equity, and privacy implications of exposome monitoring, as well as actionable pathways for translating scientific findings into effective policy and clinical practice.By spanning technology development, computational innovation, and practical applications, this Research Topic will chart a path toward a more holistic and equitable understanding of the environmental determinants of health. Topic editors: Ciro Fernando Bustillo LeCompte, Karen Soldatic, Lesly Patricia TEJEDA, Manuel Saba Source: https://www.frontiersin.org/research-topics/71737/revolutionizing-exposome-science-ai-and-sensor-technologies-for-health-and-equity