Artificial Intelligence‐Powered One Health: A Predictive Framework for Managing Persistent Chemical Threats across Human, Animal, and Environmental Systems
Jose L. Domingo · 2026
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Description
Jose Domingo, a solo author, proposes an artificial intelligence framework for getting ahead of persistent chemical pollution, in this 2026 review. The threats include long-lived organic pollutants, PFAS chemicals, heavy metals, and newer contaminants, and they cross between people, animals, and ecosystems. Current management, Domingo argues, stays fragmented and reactive, which limits the One Health idea that ties those worlds together.
Reviewing the literature to late 2025, he gathers what machine learning, deep learning, geospatial AI, and physics-informed models can already do for chemical risk. His framework joins four tools. Geospatial AI tracks sources and fates in real time, and a second tool rebuilds exposure across many species. Predictive toxicology carries risk estimates across species, and AI helps choose cleanup and policy options. He also faces the governance problems, from biased algorithms to environmental justice. The aim is a shift from watching pollution to preventing it.
Catalog record
| CREATOR | Jose L. Domingo |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |