The complexity of human health necessitates a multifaceted understanding that transcends traditional disciplinary boundaries. It is increasingly evident that personal and public health outcomes are not solely dictated by genetic predispositions but are profoundly influenced by a myriad of external factors, such as environmental exposures, lifestyle choices, diet, and socio-economic determinants. However, disentangling the intricate interplay between all these factors presents a formidable challenge that requires innovative approaches rooted in Artificial Intelligence (AI) and computational methodologies.

In fact, the recent years’ advancements in AI and computational methods, and their intersection with public health, offer unprecedented opportunities to comprehend the intricate dynamics between genetic predispositions and environmental exposures, thus unraveling crucial insights into individual and population health outcomes. This workshop aims to serve as a conduit for the exchange of cutting-edge research and ideas in this rapidly expanding field.

At the heart of this workshop lies the recognition of the pivotal role played by AI algorithms, machine learning models, and computational systems in processing vast repositories of data pertaining to epigenetics, exposomics, public health, epidemiology, socioeconomics and the environment. By harnessing the power of machine learning, data mining, and advanced statistical techniques, researchers can discern intricate patterns within complex datasets, thereby elucidating the complex relationships between environmental exposures and health outcomes. Moreover, the advent of novel AI-driven technologies offers unparalleled opportunities for predictive modeling, risk stratification, and the development of personalized interventions tailored to individual susceptibilities and/or disease trajectories.

This workshop invites contributions from experts and researchers across diverse disciplines, such as computer science, epidemiology, environmental science, medical informatics and public health. We encourage the submission of original research presenting novel methodologies, empirical findings, and information systems pertaining to the integration of AI and computational methods in the realm of public health and environmental research. Topics of interest include, but are not limited to:

• AI, machine learning or data science for environmental data

• AI, machine learning or data science for public health

• Computational approaches in exposome research 

• Computational approaches to assess the impact of the social determinants of health

• New computational systems and models for public health or environmental data

• Wearable sensors for environmental monitoring

• Computational systems for air pollution exposure monitoring 

• Effects of environmental exposures on personal and/or public health

• Computational approaches to assess the health impacts of environmental policies and interventions

• AI, machine learning , data science or information systems for the support of diet and nutrition

• Telemedicine systems for patients’ exposures or lifestyle monitoring

• Computational systems for public health and epidemiological surveillance

• Analysis of the exposome effects on longitudinal disease trajectories